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2502.13753
|
SCALAR: Scientific Citation-based Live Assessment of Long-context
Academic Reasoning
|
cs.CL
|
Evaluating large language models' (LLMs) long-context understanding
capabilities remains challenging. We present SCALAR (Scientific Citation-based
Live Assessment of Long-context Academic Reasoning), a novel benchmark that
leverages academic papers and their citation networks. SCALAR features
automatic generation of high-quality ground truth labels without human
annotation, controllable difficulty levels, and a dynamic updating mechanism
that prevents data contamination. Using ICLR 2025 papers, we evaluate 8
state-of-the-art LLMs, revealing key insights about their capabilities and
limitations in processing long scientific documents across different context
lengths and reasoning types. Our benchmark provides a reliable and sustainable
way to track progress in long-context understanding as LLM capabilities evolve.
|
2502.13754
|
Capturing Rich Behavior Representations: A Dynamic Action Semantic-Aware
Graph Transformer for Video Captioning
|
cs.CV
|
Existing video captioning methods merely provide shallow or simplistic
representations of object behaviors, resulting in superficial and ambiguous
descriptions. However, object behavior is dynamic and complex. To
comprehensively capture the essence of object behavior, we propose a dynamic
action semantic-aware graph transformer. Firstly, a multi-scale temporal
modeling module is designed to flexibly learn long and short-term latent action
features. It not only acquires latent action features across time scales, but
also considers local latent action details, enhancing the coherence and
sensitiveness of latent action representations. Secondly, a visual-action
semantic aware module is proposed to adaptively capture semantic
representations related to object behavior, enhancing the richness and
accurateness of action representations. By harnessing the collaborative efforts
of these two modules,we can acquire rich behavior representations to generate
human-like natural descriptions. Finally, this rich behavior representations
and object representations are used to construct a temporal objects-action
graph, which is fed into the graph transformer to model the complex temporal
dependencies between objects and actions. To avoid adding complexity in the
inference phase, the behavioral knowledge of the objects will be distilled into
a simple network through knowledge distillation. The experimental results on
MSVD and MSR-VTT datasets demonstrate that the proposed method achieves
significant performance improvements across multiple metrics.
|
2502.13755
|
GPA: Grover Policy Agent for Generating Optimal Quantum Sensor Circuits
|
quant-ph cs.AI
|
This study proposes a GPA for designing optimal Quantum Sensor Circuits
(QSCs) to address complex quantum physics problems. The GPA consists of two
parts: the Quantum Policy Evaluation (QPE) and the Quantum Policy Improvement
(QPI). The QPE performs phase estimation to generate the search space, while
the QPI utilizes Grover search and amplitude amplification techniques to
efficiently identify an optimal policy that generates optimal QSCs. The GPA
generates QSCs by selecting sequences of gates that maximize the Quantum Fisher
Information (QFI) while minimizing the number of gates. The QSCs generated by
the GPA are capable of producing entangled quantum states, specifically the
squeezed states. High QFI indicates increased sensitivity to parameter changes,
making the circuit useful for quantum state estimation and control tasks.
Evaluation of the GPA on a QSC that consists of two qubits and a sequence of
R_x, R_y, and S gates demonstrates its efficiency in generating optimal QSCs
with a QFI of 1. Compared to existing quantum agents, the GPA achieves higher
QFI with fewer gates, demonstrating a more efficient and scalable approach to
the design of QSCs. This work illustrates the potential computational power of
quantum agents for solving quantum physics problems
|
2502.13757
|
Identifying metric structures of deep latent variable models
|
stat.ML cs.LG
|
Deep latent variable models learn condensed representations of data that,
hopefully, reflect the inner workings of the studied phenomena. Unfortunately,
these latent representations are not statistically identifiable, meaning they
cannot be uniquely determined. Domain experts, therefore, need to tread
carefully when interpreting these. Current solutions limit the lack of
identifiability through additional constraints on the latent variable model,
e.g. by requiring labeled training data, or by restricting the expressivity of
the model. We change the goal: instead of identifying the latent variables, we
identify relationships between them such as meaningful distances, angles, and
volumes. We prove this is feasible under very mild model conditions and without
additional labeled data. We empirically demonstrate that our theory results in
more reliable latent distances, offering a principled path forward in
extracting trustworthy conclusions from deep latent variable models.
|
2502.13759
|
Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and
Human-Like Reasoning Framework
|
cs.CV
|
Geolocation, the task of identifying an image's location, requires complex
reasoning and is crucial for navigation, monitoring, and cultural preservation.
However, current methods often produce coarse, imprecise, and non-interpretable
localization. A major challenge lies in the quality and scale of existing
geolocation datasets. These datasets are typically small-scale and
automatically constructed, leading to noisy data and inconsistent task
difficulty, with images that either reveal answers too easily or lack
sufficient clues for reliable inference. To address these challenges, we
introduce a comprehensive geolocation framework with three key components:
GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval,
an evaluation metric, collectively designed to address critical challenges and
drive advancements in geolocation research. At the core of this framework is
GeoComp (Geolocation Competition Dataset), a large-scale dataset collected from
a geolocation game platform involving 740K users over two years. It comprises
25 million entries of metadata and 3 million geo-tagged locations spanning much
of the globe, with each location annotated thousands to tens of thousands of
times by human users. The dataset offers diverse difficulty levels for detailed
analysis and highlights key gaps in current models. Building on this dataset,
we propose Geographical Chain-of-Thought (GeoCoT), a novel multi-step reasoning
framework designed to enhance the reasoning capabilities of Large Vision Models
(LVMs) in geolocation tasks. GeoCoT improves performance by integrating
contextual and spatial cues through a multi-step process that mimics human
geolocation reasoning. Finally, using the GeoEval metric, we demonstrate that
GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing
interpretability.
|
2502.13760
|
Muscle Activation Estimation by Optimizing the Musculoskeletal Model for
Personalized Strength and Conditioning Training
|
physics.med-ph cs.RO
|
Musculoskeletal models are pivotal in the domains of rehabilitation and
resistance training to analyze muscle conditions. However, individual
variability in musculoskeletal parameters and the immeasurability of some
internal biomechanical variables pose significant obstacles to accurate
personalized modelling. Furthermore, muscle activation estimation can be
challenging due to the inherent redundancy of the musculoskeletal system, where
multiple muscles drive a single joint. This study develops a whole-body
musculoskeletal model for strength and conditioning training and calibrates
relevant muscle parameters with an electromyography-based optimization method.
By utilizing the personalized musculoskeletal model, muscle activation can be
subsequently estimated to analyze the performance of exercises. Bench press and
deadlift are chosen for experimental verification to affirm the efficacy of
this approach.
|
2502.13763
|
Unsupervised Graph Embeddings for Session-based Recommendation with Item
Features
|
cs.IR
|
In session-based recommender systems, predictions are based on the user's
preceding behavior in the session. State-of-the-art sequential recommendation
algorithms either use graph neural networks to model sessions in a graph or
leverage the similarity of sessions by exploiting item features. In this paper,
we combine these two approaches and propose a novel method, Graph Convolutional
Network Extension (GCNext), which incorporates item features directly into the
graph representation via graph convolutional networks. GCNext creates a
feature-rich item co-occurrence graph and learns the corresponding item
embeddings in an unsupervised manner. We show on three datasets that
integrating GCNext into sequential recommendation algorithms significantly
boosts the performance of nearest-neighbor methods as well as neural network
models. Our flexible extension is easy to incorporate in state-of-the-art
methods and increases the MRR@20 by up to 12.79%.
|
2502.13764
|
An Overall Real-Time Mechanism for Classification and Quality Evaluation
of Rice
|
cs.CV cs.AI
|
Rice is one of the most widely cultivated crops globally and has been
developed into numerous varieties. The quality of rice during cultivation is
primarily determined by its cultivar and characteristics. Traditionally, rice
classification and quality assessment rely on manual visual inspection, a
process that is both time-consuming and prone to errors. However, with
advancements in machine vision technology, automating rice classification and
quality evaluation based on its cultivar and characteristics has become
increasingly feasible, enhancing both accuracy and efficiency. This study
proposes a real-time evaluation mechanism for comprehensive rice grain
assessment, integrating a one-stage object detection approach, a deep
convolutional neural network, and traditional machine learning techniques. The
proposed framework enables rice variety identification, grain completeness
grading, and grain chalkiness evaluation. The rice grain dataset used in this
study comprises approximately 20,000 images from six widely cultivated rice
varieties in China. Experimental results demonstrate that the proposed
mechanism achieves a mean average precision (mAP) of 99.14% in the object
detection task and an accuracy of 97.89% in the classification task.
Furthermore, the framework attains an average accuracy of 97.56% in grain
completeness grading within the same rice variety, contributing to an effective
quality evaluation system.
|
2502.13766
|
GIMMICK -- Globally Inclusive Multimodal Multitask Cultural Knowledge
Benchmarking
|
cs.CL
|
Large Vision-Language Models (LVLMs) have recently gained attention due to
their distinctive performance and broad applicability. While it has been
previously shown that their efficacy in usage scenarios involving non-Western
contexts falls short, existing studies are limited in scope, covering just a
narrow range of cultures, focusing exclusively on a small number of cultural
aspects, or evaluating a limited selection of models on a single task only.
Towards globally inclusive LVLM research, we introduce GIMMICK, an extensive
multimodal benchmark designed to assess a broad spectrum of cultural knowledge
across 144 countries representing six global macro-regions. GIMMICK comprises
six tasks built upon three new datasets that span 728 unique cultural events or
facets on which we evaluated 20 LVLMs and 11 LLMs, including five proprietary
and 26 open-weight models of all sizes. We systematically examine (1) regional
cultural biases, (2) the influence of model size, (3) input modalities, and (4)
external cues. Our analyses reveal strong biases toward Western cultures across
models and tasks and highlight strong correlations between model size and
performance, as well as the effectiveness of multimodal input and external
geographic cues. We further find that models have more knowledge of tangible
than intangible aspects (e.g., food vs. rituals) and that they excel in
recognizing broad cultural origins but struggle with a more nuanced
understanding.
|
2502.13767
|
AI Software Engineer: Programming with Trust
|
cs.SE cs.AI
|
Large Language Models (LLMs) have shown surprising proficiency in generating
code snippets, promising to automate large parts of software engineering via
artificial intelligence (AI). We argue that successfully deploying AI software
engineers requires a level of trust equal to or even greater than the trust
established by human-driven software engineering practices. The recent trend
toward LLM agents offers a path toward integrating the power of LLMs to create
new code with the power of analysis tools to increase trust in the code. This
opinion piece comments on whether LLM agents could dominate software
engineering workflows in the future and whether the focus of programming will
shift from programming at scale to programming with trust.
|
2502.13769
|
A consensus set for the aggregation of partial rankings: the case of the
Optimal Set of Bucket Orders Problem
|
cs.AI
|
In rank aggregation problems (RAP), the solution is usually a consensus
ranking that generalizes a set of input orderings. There are different variants
that differ not only in terms of the type of rankings that are used as input
and output, but also in terms of the objective function employed to evaluate
the quality of the desired output ranking. In contrast, in some machine
learning tasks (e.g. subgroup discovery) or multimodal optimization tasks,
attention is devoted to obtaining several models/results to account for the
diversity in the input data or across the search landscape. Thus, in this paper
we propose to provide, as the solution to an RAP, a set of rankings to better
explain the preferences expressed in the input orderings. We exemplify our
proposal through the Optimal Bucket Order Problem (OBOP), an RAP which consists
in finding a single consensus ranking (with ties) that generalizes a set of
input rankings codified as a precedence matrix. To address this, we introduce
the Optimal Set of Bucket Orders Problem (OSBOP), a generalization of the OBOP
that aims to produce not a single ranking as output but a set of consensus
rankings. Experimental results are presented to illustrate this proposal,
showing how, by providing a set of consensus rankings, the fitness of the
solution significantly improves with respect to the one of the original OBOP,
without losing comprehensibility.
|
2502.13773
|
Multi-Covering a Point Set by $m$ Disks with Minimum Total Area
|
cs.RO cs.CG
|
A common robotics sensing problem is to place sensors to robustly monitor a
set of assets, where robustness is assured by requiring asset $p$ to be
monitored by at least $\kappa(p)$ sensors. Given $n$ assets that must be
observed by $m$ sensors, each with a disk-shaped sensing region, where should
the sensors be placed to minimize the total area observed? We provide and
analyze a fast heuristic for this problem. We then use the heuristic to
initialize an exact Integer Programming solution. Subsequently, we enforce
separation constraints between the sensors by modifying the integer program
formulation and by changing the disk candidate set.
|
2502.13775
|
VITAL: A New Dataset for Benchmarking Pluralistic Alignment in
Healthcare
|
cs.CL cs.AI cs.LG
|
Alignment techniques have become central to ensuring that Large Language
Models (LLMs) generate outputs consistent with human values. However, existing
alignment paradigms often model an averaged or monolithic preference, failing
to account for the diversity of perspectives across cultures, demographics, and
communities. This limitation is particularly critical in health-related
scenarios, where plurality is essential due to the influence of culture,
religion, personal values, and conflicting opinions. Despite progress in
pluralistic alignment, no prior work has focused on health, likely due to the
unavailability of publicly available datasets. To address this gap, we
introduce VITAL, a new benchmark dataset comprising 13.1K value-laden
situations and 5.4K multiple-choice questions focused on health, designed to
assess and benchmark pluralistic alignment methodologies. Through extensive
evaluation of eight LLMs of varying sizes, we demonstrate that existing
pluralistic alignment techniques fall short in effectively accommodating
diverse healthcare beliefs, underscoring the need for tailored AI alignment in
specific domains. This work highlights the limitations of current approaches
and lays the groundwork for developing health-specific alignment solutions.
|
2502.13776
|
EHOP: A Dataset of Everyday NP-Hard Optimization Problems
|
cs.CL cs.CC
|
We introduce the dataset of Everyday Hard Optimization Problems (EHOP), a
collection of NP-hard optimization problems expressed in natural language. EHOP
includes problem formulations that could be found in computer science
textbooks, versions that are dressed up as problems that could arise in real
life, and variants of well-known problems with inverted rules. We find that
state-of-the-art LLMs, across multiple prompting strategies, systematically
solve textbook problems more accurately than their real-life and inverted
counterparts. We argue that this constitutes evidence that LLMs adapt solutions
seen during training, rather than leveraging reasoning abilities that would
enable them to generalize to novel problems.
|
2502.13777
|
Herglotz-NET: Implicit Neural Representation of Spherical Data with
Harmonic Positional Encoding
|
cs.LG eess.SP
|
Representing and processing data in spherical domains presents unique
challenges, primarily due to the curvature of the domain, which complicates the
application of classical Euclidean techniques. Implicit neural representations
(INRs) have emerged as a promising alternative for high-fidelity data
representation; however, to effectively handle spherical domains, these methods
must be adapted to the inherent geometry of the sphere to maintain both
accuracy and stability. In this context, we propose Herglotz-NET (HNET), a
novel INR architecture that employs a harmonic positional encoding based on
complex Herglotz mappings. This encoding yields a well-posed representation on
the sphere with interpretable and robust spectral properties. Moreover, we
present a unified expressivity analysis showing that any spherical-based INR
satisfying a mild condition exhibits a predictable spectral expansion that
scales with network depth. Our results establish HNET as a scalable and
flexible framework for accurate modeling of spherical data.
|
2502.13778
|
Poster: SpiderSim: Multi-Agent Driven Theoretical Cybersecurity
Simulation for Industrial Digitalization
|
cs.CR cs.AI
|
Rapid industrial digitalization has created intricate cybersecurity demands
that necessitate effective validation methods. While cyber ranges and
simulation platforms are widely deployed, they frequently face limitations in
scenario diversity and creation efficiency. In this paper, we present
SpiderSim, a theoretical cybersecurity simulation platform enabling rapid and
lightweight scenario generation for industrial digitalization security
research. At its core, our platform introduces three key innovations: a
structured framework for unified scenario modeling, a multi-agent collaboration
mechanism for automated generation, and modular atomic security capabilities
for flexible scenario composition. Extensive implementation trials across
multiple industrial digitalization contexts, including marine ranch monitoring
systems, validate our platform's capacity for broad scenario coverage with
efficient generation processes. Built on solid theoretical foundations and
released as open-source software, SpiderSim facilitates broader research and
development in automated security testing for industrial digitalization.
|
2502.13780
|
Translation in the Hands of Many:Centering Lay Users in Machine
Translation Interactions
|
cs.CL cs.CY
|
Converging societal and technical factors have transformed language
technologies into user-facing applications employed across languages. Machine
Translation (MT) has become a global tool, with cross-lingual services now also
supported by dialogue systems powered by multilingual Large Language Models
(LLMs). This accessibility has expanded MT's reach to a vast base of lay users,
often with little to no expertise in the languages or the technology itself.
Despite this, the understanding of MT consumed by this diverse group of users
-- their needs, experiences, and interactions with these systems -- remains
limited. This paper traces the shift in MT user profiles, focusing on
non-expert users and how their engagement with these systems may change with
LLMs. We identify three key factors -- usability, trust, and literacy -- that
shape these interactions and must be addressed to align MT with user needs. By
exploring these dimensions, we offer insights to guide future MT with a
user-centered approach.
|
2502.13783
|
Generative Large Recommendation Models: Emerging Trends in LLMs for
Recommendation
|
cs.IR
|
In the era of information overload, recommendation systems play a pivotal
role in filtering data and delivering personalized content. Recent advancements
in feature interaction and user behavior modeling have significantly enhanced
the recall and ranking processes of these systems. With the rise of large
language models (LLMs), new opportunities have emerged to further improve
recommendation systems. This tutorial explores two primary approaches for
integrating LLMs: LLMs-enhanced recommendations, which leverage the reasoning
capabilities of general LLMs, and generative large recommendation models, which
focus on scaling and sophistication. While the former has been extensively
covered in existing literature, the latter remains underexplored. This tutorial
aims to fill this gap by providing a comprehensive overview of generative large
recommendation models, including their recent advancements, challenges, and
potential research directions. Key topics include data quality, scaling laws,
user behavior mining, and efficiency in training and inference. By engaging
with this tutorial, participants will gain insights into the latest
developments and future opportunities in the field, aiding both academic
research and practical applications. The timely nature of this exploration
supports the rapid evolution of recommendation systems, offering valuable
guidance for researchers and practitioners alike.
|
2502.13785
|
Helix-mRNA: A Hybrid Foundation Model For Full Sequence mRNA
Therapeutics
|
q-bio.GN cs.AI
|
mRNA-based vaccines have become a major focus in the pharmaceutical industry.
The coding sequence as well as the Untranslated Regions (UTRs) of an mRNA can
strongly influence translation efficiency, stability, degradation, and other
factors that collectively determine a vaccine's effectiveness. However,
optimizing mRNA sequences for those properties remains a complex challenge.
Existing deep learning models often focus solely on coding region optimization,
overlooking the UTRs. We present Helix-mRNA, a structured state-space-based and
attention hybrid model to address these challenges. In addition to a first
pre-training, a second pre-training stage allows us to specialise the model
with high-quality data. We employ single nucleotide tokenization of mRNA
sequences with codon separation, ensuring prior biological and structural
information from the original mRNA sequence is not lost. Our model, Helix-mRNA,
outperforms existing methods in analysing both UTRs and coding region
properties. It can process sequences 6x longer than current approaches while
using only 10% of the parameters of existing foundation models. Its predictive
capabilities extend to all mRNA regions. We open-source the model
(https://github.com/helicalAI/helical) and model weights
(https://huggingface.co/helical-ai/helix-mRNA).
|
2502.13789
|
From Correctness to Comprehension: AI Agents for Personalized Error
Diagnosis in Education
|
cs.CV
|
Large Language Models (LLMs), such as GPT-4, have demonstrated impressive
mathematical reasoning capabilities, achieving near-perfect performance on
benchmarks like GSM8K. However, their application in personalized education
remains limited due to an overemphasis on correctness over error diagnosis and
feedback generation. Current models fail to provide meaningful insights into
the causes of student mistakes, limiting their utility in educational contexts.
To address these challenges, we present three key contributions. First, we
introduce \textbf{MathCCS} (Mathematical Classification and Constructive
Suggestions), a multi-modal benchmark designed for systematic error analysis
and tailored feedback. MathCCS includes real-world problems, expert-annotated
error categories, and longitudinal student data. Evaluations of
state-of-the-art models, including \textit{Qwen2-VL}, \textit{LLaVA-OV},
\textit{Claude-3.5-Sonnet} and \textit{GPT-4o}, reveal that none achieved
classification accuracy above 30\% or generated high-quality suggestions
(average scores below 4/10), highlighting a significant gap from human-level
performance. Second, we develop a sequential error analysis framework that
leverages historical data to track trends and improve diagnostic precision.
Finally, we propose a multi-agent collaborative framework that combines a Time
Series Agent for historical analysis and an MLLM Agent for real-time
refinement, enhancing error classification and feedback generation. Together,
these contributions provide a robust platform for advancing personalized
education, bridging the gap between current AI capabilities and the demands of
real-world teaching.
|
2502.13791
|
From Tools to Teammates: Evaluating LLMs in Multi-Session Coding
Interactions
|
cs.CL
|
Large Language Models (LLMs) are increasingly used in working environments
for a wide range of tasks, excelling at solving individual problems in
isolation. However, are they also able to effectively collaborate over
long-term interactions? To investigate this, we introduce MemoryCode, a
synthetic multi-session dataset designed to test LLMs' ability to track and
execute simple coding instructions amid irrelevant information, simulating a
realistic setting. While all the models we tested handle isolated instructions
well, even the performance of state-of-the-art models like GPT-4o deteriorates
when instructions are spread across sessions. Our analysis suggests this is due
to their failure to retrieve and integrate information over long instruction
chains. Our results highlight a fundamental limitation of current LLMs,
restricting their ability to collaborate effectively in long interactions.
|
2502.13794
|
LESA: Learnable LLM Layer Scaling-Up
|
cs.LG cs.AI cs.CL
|
Training Large Language Models (LLMs) from scratch requires immense
computational resources, making it prohibitively expensive. Model scaling-up
offers a promising solution by leveraging the parameters of smaller models to
create larger ones. However, existing depth scaling-up methods rely on
empirical heuristic rules for layer duplication, which result in poorer
initialization and slower convergence during continual pre-training. We propose
\textbf{LESA}, a novel learnable method for depth scaling-up. By concatenating
parameters from each layer and applying Singular Value Decomposition, we
uncover latent patterns between layers, suggesting that inter-layer parameters
can be learned. LESA uses a neural network to predict the parameters inserted
between adjacent layers, enabling better initialization and faster training.
Experiments show that LESA outperforms existing baselines, achieving superior
performance with less than half the computational cost during continual
pre-training. Extensive analyses demonstrate its effectiveness across different
model sizes and tasks.
|
2502.13801
|
Learning to explore when mistakes are not allowed
|
cs.LG cs.SY eess.SY
|
Goal-Conditioned Reinforcement Learning (GCRL) provides a versatile framework
for developing unified controllers capable of handling wide ranges of tasks,
exploring environments, and adapting behaviors. However, its reliance on
trial-and-error poses challenges for real-world applications, as errors can
result in costly and potentially damaging consequences. To address the need for
safer learning, we propose a method that enables agents to learn
goal-conditioned behaviors that explore without the risk of making harmful
mistakes. Exploration without risks can seem paradoxical, but environment
dynamics are often uniform in space, therefore a policy trained for safety
without exploration purposes can still be exploited globally. Our proposed
approach involves two distinct phases. First, during a pretraining phase, we
employ safe reinforcement learning and distributional techniques to train a
safety policy that actively tries to avoid failures in various situations. In
the subsequent safe exploration phase, a goal-conditioned (GC) policy is
learned while ensuring safety. To achieve this, we implement an
action-selection mechanism leveraging the previously learned distributional
safety critics to arbitrate between the safety policy and the GC policy,
ensuring safe exploration by switching to the safety policy when needed. We
evaluate our method in simulated environments and demonstrate that it not only
provides substantial coverage of the goal space but also reduces the occurrence
of mistakes to a minimum, in stark contrast to traditional GCRL approaches.
Additionally, we conduct an ablation study and analyze failure modes, offering
insights for future research directions.
|
2502.13803
|
3D Gaussian Splatting aided Localization for Large and Complex
Indoor-Environments
|
cs.CV cs.RO
|
The field of visual localization has been researched for several decades and
has meanwhile found many practical applications. Despite the strong progress in
this field, there are still challenging situations in which established methods
fail. We present an approach to significantly improve the accuracy and
reliability of established visual localization methods by adding rendered
images. In detail, we first use a modern visual SLAM approach that provides a
3D Gaussian Splatting (3DGS) based map to create reference data. We demonstrate
that enriching reference data with images rendered from 3DGS at randomly
sampled poses significantly improves the performance of both geometry-based
visual localization and Scene Coordinate Regression (SCR) methods. Through
comprehensive evaluation in a large industrial environment, we analyze the
performance impact of incorporating these additional rendered views.
|
2502.13805
|
AnDB: Breaking Boundaries with an AI-Native Database for Universal
Semantic Analysis
|
cs.DB cs.AI cs.LG
|
In this demonstration, we present AnDB, an AI-native database that supports
traditional OLTP workloads and innovative AI-driven tasks, enabling unified
semantic analysis across structured and unstructured data. While structured
data analytics is mature, challenges remain in bridging the semantic gap
between user queries and unstructured data. AnDB addresses these issues by
leveraging cutting-edge AI-native technologies, allowing users to perform
semantic queries using intuitive SQL-like statements without requiring AI
expertise. This approach eliminates the ambiguity of traditional text-to-SQL
systems and provides a seamless end-to-end optimization for analyzing all data
types. AnDB automates query processing by generating multiple execution plans
and selecting the optimal one through its optimizer, which balances accuracy,
execution time, and financial cost based on user policies and internal
optimizing mechanisms. AnDB future-proofs data management infrastructure,
empowering users to effectively and efficiently harness the full potential of
all kinds of data without starting from scratch.
|
2502.13808
|
MGFI-Net: A Multi-Grained Feature Integration Network for Enhanced
Medical Image Segmentation
|
eess.IV cs.CV
|
Medical image segmentation plays a crucial role in various clinical
applications. A major challenge in medical image segmentation is achieving
accurate delineation of regions of interest in the presence of noise, low
contrast, or complex anatomical structures. Existing segmentation models often
neglect the integration of multi-grained information and fail to preserve edge
details, which are critical for precise segmentation. To address these
challenges, we propose a novel image semantic segmentation model called the
Multi-Grained Feature Integration Network (MGFI-Net). Our MGFI-Net is designed
with two dedicated modules to tackle these issues. First, to enhance
segmentation accuracy, we introduce a Multi-Grained Feature Extraction Module,
which leverages hierarchical relationships between different feature scales to
selectively focus on the most relevant information. Second, to preserve edge
details, we incorporate an Edge Enhancement Module that effectively retains and
integrates boundary information to refine segmentation results. Extensive
experiments demonstrate that MGFI-Net not only outperforms state-of-the-art
methods in terms of segmentation accuracy but also achieves superior time
efficiency, establishing it as a leading solution for real-time medical image
segmentation.
|
2502.13810
|
Learning Is a Kan Extension
|
math.CT cs.LG
|
Previous work has demonstrated that efficient algorithms exist for computing
Kan extensions and that some Kan extensions have interesting similarities to
various machine learning algorithms. This paper closes the gap by proving that
all error minimisation algorithms may be presented as a Kan extension. This
result provides a foundation for future work to investigate the optimisation of
machine learning algorithms through their presentation as Kan extensions. A
corollary of this representation of error-minimising algorithms is a
presentation of error from the perspective of lossy and lossless
transformations of data.
|
2502.13811
|
On the Duality between Gradient Transformations and Adapters
|
cs.LG cs.CL
|
We study memory-efficient optimization of neural networks with linear
gradient transformations, where the gradients are linearly mapped to a lower
dimensional space than the full parameter space, thus saving memory required
for gradient accumulation and optimizer state persistence. The model parameters
are updated by first performing an optimization step in the lower dimensional
space and then going back into the original parameter space via the linear
map's transpose. We show that optimizing the model in this transformed space is
equivalent to reparameterizing the original model through a linear adapter that
additively modifies the model parameters, and then only optimizing the
adapter's parameters. When the transformation is Kronecker-factored, this
establishes an equivalence between GaLore and one-sided LoRA. We show that this
duality between gradient transformations and adapter-based reparameterizations
unifies existing approaches to memory-efficient training and suggests new
techniques for improving training efficiency and memory use.
|
2502.13813
|
Optimal Overlap Detection of Shotgun Reads
|
cs.IT math.IT math.ST stat.TH
|
We consider the problem of detecting the overlap between a pair of short
fragments sampled in random locations from an exponentially longer sequence,
via their possibly noisy reads. We consider a noiseless setting, in which the
reads are noiseless, and the sequence is only assumed to be stationary and
ergodic. Under mild conditions on the mixing property of the process generating
the sequence, we characterize exactly the asymptotic error probability of the
optimal Bayesian detector. Similarly, we consider a noisy setting, in which the
reads are noisy versions of the sampled fragments obtained via a memoryless
channel. We further assume that the sequence is stationary and memoryless, and
similarly characterize exactly the asymptotic error probability of the optimal
Bayesian detector for this case.
|
2502.13816
|
Exploring Embodied Emotional Communication: A Human-oriented Review of
Mediated Social Touch
|
cs.HC cs.RO
|
This paper offers a structured understanding of mediated social touch (MST)
using a human-oriented approach, through an extensive review of literature
spanning tactile interfaces, emotional information, mapping mechanisms, and the
dynamics of human-human and human-robot interactions. By investigating the
existing and exploratory mapping strategies of the 37 selected MST cases, we
established the emotional expression space of MSTs that accommodated a diverse
spectrum of emotions by integrating the categorical and Valence-arousal models,
showcasing how emotional cues can be translated into tactile signals. Based on
the expressive capacity of MSTs, a practical design space was structured
encompassing factors such as the body locations, device form, tactile
modalities, and parameters. We also proposed various design strategies for MSTs
including workflow, evaluation methods, and ethical and cultural
considerations, as well as several future research directions. MSTs' potential
is reflected not only in conveying emotional information but also in fostering
empathy, comfort, and connection in both human-human and human-robot
interactions. This paper aims to serve as a comprehensive reference for design
researchers and practitioners, which helps expand the scope of emotional
communication of MSTs, facilitating the exploration of diverse applications of
affective haptics, and enhancing the naturalness and sociability of haptic
interaction.
|
2502.13818
|
Building Age Estimation: A New Multi-Modal Benchmark Dataset and
Community Challenge
|
cs.CV cs.LG
|
Estimating the construction year of buildings is of great importance for
sustainability. Sustainable buildings minimize energy consumption and are a key
part of responsible and sustainable urban planning and development to
effectively combat climate change. By using Artificial Intelligence (AI) and
recently proposed Transformer models, we are able to estimate the construction
epoch of buildings from a multi-modal dataset. In this paper, we introduce a
new benchmark multi-modal dataset, i.e. the Map your City Dataset (MyCD),
containing top-view Very High Resolution (VHR) images, Earth Observation (EO)
multi-spectral data from the Copernicus Sentinel-2 satellite constellation, and
street-view images in many different cities in Europe, co-localized with
respect to the building under study and labelled with the construction epoch.
We assess EO generalization performance on new/ previously unseen cities that
have been held-out from training and appear only during inference. In this
work, we present the community-based data challenge we organized based on MyCD.
The ESA AI4EO Challenge MapYourCity was opened in 2024 for 4 months. Here, we
present the Top-4 performing models, and the main evaluation results. During
inference, the performance of the models using both all three input modalities
and only the two top-view modalities, i.e. without the street-view images, is
examined. The evaluation results show that the models are effective and can
achieve good performance on this difficult real-world task of estimating the
age of buildings, even on previously unseen cities, as well as even using only
the two top-view modalities (i.e. VHR and Sentinel-2) during inference.
|
2502.13820
|
Scoring Verifiers: Evaluating Synthetic Verification in Code and
Reasoning
|
cs.AI cs.CL cs.LG cs.SE
|
Code verification has recently found great success as a critical component in
training large scale reasoning models for coding. Synthetic techniques such as
self-generated test cases and reward models provide a way to enhance code
capabilities beyond predefined tests. Building on these advancements, we
propose new benchmarks designed to systematically evaluate the impact of
synthetic verification methods on assessing solution correctness. We introduce
HE-R, HE-R+, MBPP-R, and MBPP-R+, which transform existing coding benchmarks
into scoring and ranking datasets to evaluate the effectiveness of synthetic
verifiers. Using these benchmarks, we analyze synthetic verification methods in
standard, reasoning-based, and reward-based LLMs. Our results show that recent
reasoning models significantly improve test case generation and that scaling
test cases enhances verification accuracy.
|
2502.13822
|
Uncertainty quantification for Markov chains with application to
temporal difference learning
|
stat.ML cs.LG
|
Markov chains are fundamental to statistical machine learning, underpinning
key methodologies such as Markov Chain Monte Carlo (MCMC) sampling and temporal
difference (TD) learning in reinforcement learning (RL). Given their widespread
use, it is crucial to establish rigorous probabilistic guarantees on their
convergence, uncertainty, and stability. In this work, we develop novel,
high-dimensional concentration inequalities and Berry-Esseen bounds for vector-
and matrix-valued functions of Markov chains, addressing key limitations in
existing theoretical tools for handling dependent data. We leverage these
results to analyze the TD learning algorithm, a widely used method for policy
evaluation in RL. Our analysis yields a sharp high-probability consistency
guarantee that matches the asymptotic variance up to logarithmic factors.
Furthermore, we establish a $O(T^{-\frac{1}{4}}\log T)$ distributional
convergence rate for the Gaussian approximation of the TD estimator, measured
in convex distance. These findings provide new insights into statistical
inference for RL algorithms, bridging the gaps between classical stochastic
approximation theory and modern reinforcement learning applications.
|
2502.13823
|
An Online Optimization-Based Trajectory Planning Approach for
Cooperative Landing Tasks
|
cs.RO
|
This paper presents a real-time trajectory planning scheme for a
heterogeneous multi-robot system (consisting of a quadrotor and a ground mobile
robot) for a cooperative landing task, where the landing position, landing
time, and coordination between the robots are determined autonomously under the
consideration of feasibility and user specifications. The proposed framework
leverages the potential of the complementarity constraint as a decision-maker
and an indicator for diverse cooperative tasks and extends it to the
collaborative landing scenario. In a potential application of the proposed
methodology, a ground mobile robot may serve as a mobile charging station and
coordinates in real-time with a quadrotor to be charged, facilitating a safe
and efficient rendezvous and landing. We verified the generated trajectories in
simulation and real-world applications, demonstrating the real-time
capabilities of the proposed landing planning framework.
|
2502.13825
|
Mixup Regularization: A Probabilistic Perspective
|
cs.LG stat.ML
|
In recent years, mixup regularization has gained popularity as an effective
way to improve the generalization performance of deep learning models by
training on convex combinations of training data. While many mixup variants
have been explored, the proper adoption of the technique to conditional density
estimation and probabilistic machine learning remains relatively unexplored.
This work introduces a novel framework for mixup regularization based on
probabilistic fusion that is better suited for conditional density estimation
tasks. For data distributed according to a member of the exponential family, we
show that likelihood functions can be analytically fused using log-linear
pooling. We further propose an extension of probabilistic mixup, which allows
for fusion of inputs at an arbitrary intermediate layer of the neural network.
We provide a theoretical analysis comparing our approach to standard mixup
variants. Empirical results on synthetic and real datasets demonstrate the
benefits of our proposed framework compared to existing mixup variants.
|
2502.13826
|
In-Place Updates of a Graph Index for Streaming Approximate Nearest
Neighbor Search
|
cs.IR
|
Indices for approximate nearest neighbor search (ANNS) are a basic component
for information retrieval and widely used in database, search, recommendation
and RAG systems. In these scenarios, documents or other objects are inserted
into and deleted from the working set at a high rate, requiring a stream of
updates to the vector index. Algorithms based on proximity graph indices are
the most efficient indices for ANNS, winning many benchmark competitions.
However, it is challenging to update such graph index at a high rate, while
supporting stable recall after many updates. Since the graph is singly-linked,
deletions are hard because there is no fast way to find in-neighbors of a
deleted vertex. Therefore, to update the graph, state-of-the-art algorithms
such as FreshDiskANN accumulate deletions in a batch and periodically
consolidate, removing edges to deleted vertices and modifying the graph to
ensure recall stability. In this paper, we present IP-DiskANN
(InPlaceUpdate-DiskANN), the first algorithm to avoid batch consolidation by
efficiently processing each insertion and deletion in-place. Our experiments
using standard benchmarks show that IP-DiskANN has stable recall over various
lengthy update patterns in both high-recall and low-recall regimes. Further,
its query throughput and update speed are better than using the batch
consolidation algorithm and HNSW.
|
2502.13827
|
Bayesian Physics Informed Neural Networks for Linear Inverse problems
|
cs.LG cs.NA math.NA
|
Inverse problems arise almost everywhere in science and engineering where we
need to infer on a quantity from indirect observation. The cases of medical,
biomedical, and industrial imaging systems are the typical examples. A very
high overview of classification of the inverse problems method can be: i)
Analytical, ii) Regularization, and iii) Bayesian inference methods. Even if
there are straight links between them, we can say that the Bayesian inference
based methods are the most powerful, as they give the possibility of accounting
for prior knowledge and can account for errors and uncertainties in general.
One of the main limitations stay in computational costs in particular for high
dimensional imaging systems. Neural Networks (NN), and in particular Deep NNs
(DNN), have been considered as a way to push farther this limit. Physics
Informed Neural Networks (PINN) concept integrates physical laws with deep
learning techniques to enhance the speed, accuracy and efficiency of the above
mentioned problems.
In this work, a new Bayesian framework for the concept of PINN (BPINN) is
presented and discussed which includes the deterministic one if we use the
Maximum A Posteriori (MAP) estimation framework. We consider two cases of
supervised and unsupervised for training step, obtain the expressions of the
posterior probability of the unknown variables, and deduce the posterior laws
of the NN's parameters. We also discuss about the challenges of implementation
of these methods in real applications.
|
2502.13833
|
Contrastive Learning-Based privacy metrics in Tabular Synthetic Datasets
|
cs.LG cs.CR
|
Synthetic data has garnered attention as a Privacy Enhancing Technology (PET)
in sectors such as healthcare and finance. When using synthetic data in
practical applications, it is important to provide protection guarantees. In
the literature, two family of approaches are proposed for tabular data: on the
one hand, Similarity-based methods aim at finding the level of similarity
between training and synthetic data. Indeed, a privacy breach can occur if the
generated data is consistently too similar or even identical to the train data.
On the other hand, Attack-based methods conduce deliberate attacks on synthetic
datasets. The success rates of these attacks reveal how secure the synthetic
datasets are.
In this paper, we introduce a contrastive method that improves privacy
assessment of synthetic datasets by embedding the data in a more representative
space. This overcomes obstacles surrounding the multitude of data types and
attributes. It also makes the use of intuitive distance metrics possible for
similarity measurements and as an attack vector. In a series of experiments
with publicly available datasets, we compare the performances of
similarity-based and attack-based methods, both with and without use of the
contrastive learning-based embeddings. Our results show that relatively
efficient, easy to implement privacy metrics can perform equally well as more
advanced metrics explicitly modeling conditions for privacy referred to by the
GDPR.
|
2502.13834
|
Proving Olympiad Inequalities by Synergizing LLMs and Symbolic Reasoning
|
cs.AI
|
Large language models (LLMs) can prove mathematical theorems formally by
generating proof steps (\textit{a.k.a.} tactics) within a proof system.
However, the space of possible tactics is vast and complex, while the available
training data for formal proofs is limited, posing a significant challenge to
LLM-based tactic generation. To address this, we introduce a neuro-symbolic
tactic generator that synergizes the mathematical intuition learned by LLMs
with domain-specific insights encoded by symbolic methods. The key aspect of
this integration is identifying which parts of mathematical reasoning are best
suited to LLMs and which to symbolic methods. While the high-level idea of
neuro-symbolic integration is broadly applicable to various mathematical
problems, in this paper, we focus specifically on Olympiad inequalities
(Figure~1). We analyze how humans solve these problems and distill the
techniques into two types of tactics: (1) scaling, handled by symbolic methods,
and (2) rewriting, handled by LLMs. In addition, we combine symbolic tools with
LLMs to prune and rank the proof goals for efficient proof search. We evaluate
our framework on 161 challenging inequalities from multiple mathematics
competitions, achieving state-of-the-art performance and significantly
outperforming existing LLM and symbolic approaches without requiring additional
training data.
|
2502.13836
|
Quantifying Memorization and Retriever Performance in
Retrieval-Augmented Vision-Language Models
|
cs.LG cs.AI
|
Large Language Models (LLMs) demonstrate remarkable capabilities in question
answering (QA), but metrics for assessing their reliance on memorization versus
retrieval remain underdeveloped. Moreover, while finetuned models are
state-of-the-art on closed-domain tasks, general-purpose models like GPT-4o
exhibit strong zero-shot performance. This raises questions about the
trade-offs between memorization, generalization, and retrieval. In this work,
we analyze the extent to which multimodal retrieval-augmented VLMs memorize
training data compared to baseline VLMs. Using the WebQA benchmark, we contrast
finetuned models with baseline VLMs on multihop retrieval and question
answering, examining the impact of finetuning on data memorization. To quantify
memorization in end-to-end retrieval and QA systems, we propose several proxy
metrics by investigating instances where QA succeeds despite retrieval failing.
Our results reveal the extent to which finetuned models rely on memorization.
In contrast, retrieval-augmented VLMs have lower memorization scores, at the
cost of accuracy (72% vs 52% on WebQA test set). As such, our measures pose a
challenge for future work to reconcile memorization and generalization in both
Open-Domain QA and joint Retrieval-QA tasks.
|
2502.13838
|
Generative Video Semantic Communication via Multimodal Semantic Fusion
with Large Model
|
eess.SP cs.CV cs.IT eess.IV math.IT
|
Despite significant advancements in traditional syntactic communications
based on Shannon's theory, these methods struggle to meet the requirements of
6G immersive communications, especially under challenging transmission
conditions. With the development of generative artificial intelligence (GenAI),
progress has been made in reconstructing videos using high-level semantic
information. In this paper, we propose a scalable generative video semantic
communication framework that extracts and transmits semantic information to
achieve high-quality video reconstruction. Specifically, at the transmitter,
description and other condition signals (e.g., first frame, sketches, etc.) are
extracted from the source video, functioning as text and structural semantics,
respectively. At the receiver, the diffusion-based GenAI large models are
utilized to fuse the semantics of the multiple modalities for reconstructing
the video. Simulation results demonstrate that, at an ultra-low channel
bandwidth ratio (CBR), our scheme effectively captures semantic information to
reconstruct videos aligned with human perception under different
signal-to-noise ratios. Notably, the proposed ``First Frame+Desc." scheme
consistently achieves CLIP score exceeding 0.92 at CBR = 0.0057 for SNR > 0 dB.
This demonstrates its robust performance even under low SNR conditions.
|
2502.13840
|
Mitigating Popularity Bias in Collaborative Filtering through Fair
Sampling
|
cs.IR cs.AI
|
Recommender systems often suffer from popularity bias, where frequently
interacted items are overrepresented in recommendations. This bias stems from
propensity factors influencing training data, leading to imbalanced exposure.
In this paper, we introduce a Fair Sampling (FS) approach to address this issue
by ensuring that both users and items are selected with equal probability as
positive and negative instances. Unlike traditional inverse propensity score
(IPS) methods, FS does not require propensity estimation, eliminating errors
associated with inaccurate calculations. Our theoretical analysis demonstrates
that FS effectively neutralizes the influence of propensity factors, achieving
unbiased learning. Experimental results validate that FS outperforms
state-of-the-art methods in both point-wise and pair-wise recommendation tasks,
enhancing recommendation fairness without sacrificing accuracy. The
implementation is available at https://anonymous.4open.science/r/Fair-Sampling.
|
2502.13842
|
Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster
Adaptive Internal Thinking
|
cs.CL
|
Large language models (LLMs) face inherent performance bottlenecks under
parameter constraints, particularly in processing critical tokens that demand
complex reasoning. Empirical analysis reveals challenging tokens induce abrupt
gradient spikes across layers, exposing architectural stress points in standard
Transformers. Building on this insight, we propose Inner Thinking Transformer
(ITT), which reimagines layer computations as implicit thinking steps. ITT
dynamically allocates computation through Adaptive Token Routing, iteratively
refines representations via Residual Thinking Connections, and distinguishes
reasoning phases using Thinking Step Encoding. ITT enables deeper processing of
critical tokens without parameter expansion. Evaluations across 162M-466M
parameter models show ITT achieves 96.5\% performance of a 466M Transformer
using only 162M parameters, reduces training data by 43.2\%, and outperforms
Transformer/Loop variants in 11 benchmarks. By enabling elastic computation
allocation during inference, ITT balances performance and efficiency through
architecture-aware optimization of implicit thinking pathways.
|
2502.13843
|
Enhancing Cross-Domain Recommendations with Memory-Optimized LLM-Based
User Agents
|
cs.IR cs.AI
|
Large Language Model (LLM)-based user agents have emerged as a powerful tool
for improving recommender systems by simulating user interactions. However,
existing methods struggle with cross-domain scenarios due to inefficient memory
structures, leading to irrelevant information retention and failure to account
for social influence factors such as popularity. To address these limitations,
we introduce AgentCF++, a novel framework featuring a dual-layer memory
architecture and a two-step fusion mechanism to filter domain-specific
preferences effectively. Additionally, we propose interest groups with shared
memory, allowing the model to capture the impact of popularity trends on users
with similar interests. Through extensive experiments on multiple cross-domain
datasets, AgentCF++ demonstrates superior performance over baseline models,
highlighting its effectiveness in refining user behavior simulation for
recommender systems. Our code is available at
https://anonymous.4open.science/r/AgentCF-plus.
|
2502.13845
|
Enhancing LLM-Based Recommendations Through Personalized Reasoning
|
cs.IR cs.AI
|
Current recommendation systems powered by large language models (LLMs) often
underutilize their reasoning capabilities due to a lack of explicit logical
structuring. To address this limitation, we introduce CoT-Rec, a framework that
integrates Chain-of-Thought (CoT) reasoning into LLM-driven recommendations by
incorporating two crucial processes: user preference analysis and item
perception evaluation. CoT-Rec operates in two key phases: (1) personalized
data extraction, where user preferences and item perceptions are identified,
and (2) personalized data application, where this information is leveraged to
refine recommendations. Our experimental analysis demonstrates that CoT-Rec
improves recommendation accuracy by making better use of LLMs' reasoning
potential. The implementation is publicly available at
https://anonymous.4open.science/r/CoT-Rec.
|
2502.13847
|
DH-RAG: A Dynamic Historical Context-Powered Retrieval-Augmented
Generation Method for Multi-Turn Dialogue
|
cs.CL cs.AI cs.LG
|
Retrieval-Augmented Generation (RAG) systems have shown substantial benefits
in applications such as question answering and multi-turn dialogue
\citep{lewis2020retrieval}. However, traditional RAG methods, while leveraging
static knowledge bases, often overlook the potential of dynamic historical
information in ongoing conversations. To bridge this gap, we introduce DH-RAG,
a Dynamic Historical Context-Powered Retrieval-Augmented Generation Method for
Multi-Turn Dialogue. DH-RAG is inspired by human cognitive processes that
utilize both long-term memory and immediate historical context in
conversational responses \citep{stafford1987conversational}. DH-RAG is
structured around two principal components: a History-Learning based Query
Reconstruction Module, designed to generate effective queries by synthesizing
current and prior interactions, and a Dynamic History Information Updating
Module, which continually refreshes historical context throughout the dialogue.
The center of DH-RAG is a Dynamic Historical Information database, which is
further refined by three strategies within the Query Reconstruction Module:
Historical Query Clustering, Hierarchical Matching, and Chain of Thought
Tracking. Experimental evaluations show that DH-RAG significantly surpasses
conventional models on several benchmarks, enhancing response relevance,
coherence, and dialogue quality.
|
2502.13851
|
Evaluation of EAS directions based on TAIGA HiSCORE data using fully
connected neural networks
|
astro-ph.IM astro-ph.HE cs.LG
|
The direction of extensive air showers can be used to determine the source of
gamma quanta and plays an important role in estimating the energy of the
primary particle. The data from an array of non-imaging Cherenkov detector
stations HiSCORE in the TAIGA experiment registering the number of
photoelectrons and detection time can be used to estimate the shower direction
with high accuracy. In this work, we use artificial neural networks trained on
Monte Carlo-simulated TAIGA HiSCORE data for gamma quanta to obtain shower
direction estimates. The neural networks are multilayer perceptrons with skip
connections using partial data from several HiSCORE stations as inputs;
composite estimates are derived from multiple individual estimates by the
neural networks. We apply a two-stage algorithm in which the direction
estimates obtained in the first stage are used to transform the input data and
refine the estimates. The mean error of the final estimates is less than 0.25
degrees. The approach will be used for multimodal analysis of the data from
several types of detectors used in the TAIGA experiment.
|
2502.13852
|
Minimally sufficient structures for information-feedback policies
|
cs.RO
|
In this paper, we consider robotic tasks which require a desirable outcome to
be achieved in the physical world that the robot is embedded in and interacting
with. Accomplishing this objective requires designing a filter that maintains a
useful representation of the physical world and a policy over the filter
states. A filter is seen as the robot's perspective of the physical world based
on limited sensing, memory, and computation and it is represented as a
transition system over a space of information states. To this end, the
interactions result from the coupling of an internal and an external system, a
filter, and the physical world, respectively, through a sensor mapping and an
information-feedback policy. Within this setup, we look for sufficient
structures, that is, sufficient internal systems and sensors, for accomplishing
a given task. We establish necessary and sufficient conditions for these
structures to satisfy for information-feedback policies that can be defined
over the states of an internal system to exist. We also show that under mild
assumptions, minimal internal systems that can represent a particular
plan/policy described over the action-observation histories exist and are
unique. Finally, the results are applied to determine sufficient structures for
distance-optimal navigation in a polygonal environment.
|
2502.13853
|
Fine-grained Fallacy Detection with Human Label Variation
|
cs.CL
|
We introduce Faina, the first dataset for fallacy detection that embraces
multiple plausible answers and natural disagreement. Faina includes over 11K
span-level annotations with overlaps across 20 fallacy types on social media
posts in Italian about migration, climate change, and public health given by
two expert annotators. Through an extensive annotation study that allowed
discussion over multiple rounds, we minimize annotation errors whilst keeping
signals of human label variation. Moreover, we devise a framework that goes
beyond "single ground truth" evaluation and simultaneously accounts for
multiple (equally reliable) test sets and the peculiarities of the task, i.e.,
partial span matches, overlaps, and the varying severity of labeling errors.
Our experiments across four fallacy detection setups show that multi-task and
multi-label transformer-based approaches are strong baselines across all
settings. We release our data, code, and annotation guidelines to foster
research on fallacy detection and human label variation more broadly.
|
2502.13855
|
MagicGeo: Training-Free Text-Guided Geometric Diagram Generation
|
cs.CV
|
Geometric diagrams are critical in conveying mathematical and scientific
concepts, yet traditional diagram generation methods are often manual and
resource-intensive. While text-to-image generation has made strides in
photorealistic imagery, creating accurate geometric diagrams remains a
challenge due to the need for precise spatial relationships and the scarcity of
geometry-specific datasets. This paper presents MagicGeo, a training-free
framework for generating geometric diagrams from textual descriptions. MagicGeo
formulates the diagram generation process as a coordinate optimization problem,
ensuring geometric correctness through a formal language solver, and then
employs coordinate-aware generation. The framework leverages the strong
language translation capability of large language models, while formal
mathematical solving ensures geometric correctness. We further introduce
MagicGeoBench, a benchmark dataset of 220 geometric diagram descriptions, and
demonstrate that MagicGeo outperforms current methods in both qualitative and
quantitative evaluations. This work provides a scalable, accurate solution for
automated diagram generation, with significant implications for educational and
academic applications.
|
2502.13859
|
MSVCOD:A Large-Scale Multi-Scene Dataset for Video Camouflage Object
Detection
|
cs.CV
|
Video Camouflaged Object Detection (VCOD) is a challenging task which aims to
identify objects that seamlessly concealed within the background in videos. The
dynamic properties of video enable detection of camouflaged objects through
motion cues or varied perspectives. Previous VCOD datasets primarily contain
animal objects, limiting the scope of research to wildlife scenarios. However,
the applications of VCOD extend beyond wildlife and have significant
implications in security, art, and medical fields. Addressing this problem, we
construct a new large-scale multi-domain VCOD dataset MSVCOD. To achieve
high-quality annotations, we design a semi-automatic iterative annotation
pipeline that reduces costs while maintaining annotation accuracy. Our MSVCOD
is the largest VCOD dataset to date, introducing multiple object categories
including human, animal, medical, and vehicle objects for the first time, while
also expanding background diversity across various environments. This expanded
scope increases the practical applicability of the VCOD task in camouflaged
object detection. Alongside this dataset, we introduce a one-steam video
camouflage object detection model that performs both feature extraction and
information fusion without additional motion feature fusion modules. Our
framework achieves state-of-the-art results on the existing VCOD animal dataset
and the proposed MSVCOD. The dataset and code will be made publicly available.
|
2502.13863
|
The NavINST Dataset for Multi-Sensor Autonomous Navigation
|
cs.RO
|
The NavINST Laboratory has developed a comprehensive multisensory dataset
from various road-test trajectories in urban environments, featuring diverse
lighting conditions, including indoor garage scenarios with dense 3D maps. This
dataset includes multiple commercial-grade IMUs and a high-end tactical-grade
IMU. Additionally, it contains a wide array of perception-based sensors, such
as a solid-state LiDAR - making it one of the first datasets to do so - a
mechanical LiDAR, four electronically scanning RADARs, a monocular camera, and
two stereo cameras. The dataset also includes forward speed measurements
derived from the vehicle's odometer, along with accurately post-processed
high-end GNSS/IMU data, providing precise ground truth positioning and
navigation information. The NavINST dataset is designed to support advanced
research in high-precision positioning, navigation, mapping, computer vision,
and multisensory fusion. It offers rich, multi-sensor data ideal for developing
and validating robust algorithms for autonomous vehicles. Finally, it is fully
integrated with the ROS, ensuring ease of use and accessibility for the
research community. The complete dataset and development tools are available at
https://navinst.github.io.
|
2502.13870
|
SPEX: Scaling Feature Interaction Explanations for LLMs
|
cs.LG cs.AI cs.CL cs.IT math.IT
|
Large language models (LLMs) have revolutionized machine learning due to
their ability to capture complex interactions between input features. Popular
post-hoc explanation methods like SHAP provide marginal feature attributions,
while their extensions to interaction importances only scale to small input
lengths ($\approx 20$). We propose Spectral Explainer (SPEX), a model-agnostic
interaction attribution algorithm that efficiently scales to large input
lengths ($\approx 1000)$. SPEX exploits underlying natural sparsity among
interactions -- common in real-world data -- and applies a sparse Fourier
transform using a channel decoding algorithm to efficiently identify important
interactions. We perform experiments across three difficult long-context
datasets that require LLMs to utilize interactions between inputs to complete
the task. For large inputs, SPEX outperforms marginal attribution methods by up
to 20% in terms of faithfully reconstructing LLM outputs. Further, SPEX
successfully identifies key features and interactions that strongly influence
model output. For one of our datasets, HotpotQA, SPEX provides interactions
that align with human annotations. Finally, we use our model-agnostic approach
to generate explanations to demonstrate abstract reasoning in closed-source
LLMs (GPT-4o mini) and compositional reasoning in vision-language models.
|
2502.13873
|
NVR: Vector Runahead on NPUs for Sparse Memory Access
|
cs.AR cs.AI
|
Deep Neural Networks are increasingly leveraging sparsity to reduce the
scaling up of model parameter size. However, reducing wall-clock time through
sparsity and pruning remains challenging due to irregular memory access
patterns, leading to frequent cache misses. In this paper, we present NPU
Vector Runahead (NVR), a prefetching mechanism tailored for NPUs to address
cache miss problems in sparse DNN workloads. Rather than optimising memory
patterns with high overhead and poor portability, NVR adapts runahead execution
to the unique architecture of NPUs. NVR provides a general micro-architectural
solution for sparse DNN workloads without requiring compiler or algorithmic
support, operating as a decoupled, speculative, lightweight hardware sub-thread
alongside the NPU, with minimal hardware overhead (under 5%). NVR achieves an
average 90% reduction in cache misses compared to SOTA prefetching in
general-purpose processors, delivering 4x average speedup on sparse workloads
versus NPUs without prefetching. Moreover, we investigate the advantages of
incorporating a small cache (16KB) into the NPU combined with NVR. Our
evaluation shows that expanding this modest cache delivers 5x higher
performance benefits than increasing the L2 cache size by the same amount.
|
2502.13874
|
The KnowWhereGraph: A Large-Scale Geo-Knowledge Graph for
Interdisciplinary Knowledge Discovery and Geo-Enrichment
|
cs.DB
|
Global challenges such as food supply chain disruptions, public health
crises, and natural hazard responses require access to and integration of
diverse datasets, many of which are geospatial. Over the past few years, a
growing number of (geo)portals have been developed to address this need.
However, most existing (geo)portals are stacked by separated or sparsely
connected data "silos" impeding effective data consolidation. A new way of
sharing and reusing geospatial data is therefore urgently needed. In this work,
we introduce KnowWhereGraph, a knowledge graph-based data integration,
enrichment, and synthesis framework that not only includes schemas and data
related to human and environmental systems but also provides a suite of
supporting tools for accessing this information. The KnowWhereGraph aims to
address the challenge of data integration by building a large-scale,
cross-domain, pre-integrated, FAIR-principles-based, and AI-ready data
warehouse rooted in knowledge graphs. We highlight the design principles of
KnowWhereGraph, emphasizing the roles of space, place, and time in bridging
various data "silos". Additionally, we demonstrate multiple use cases where the
proposed geospatial knowledge graph and its associated tools empower
decision-makers to uncover insights that are often hidden within complex and
poorly interoperable datasets.
|
2502.13875
|
MEX: Memory-efficient Approach to Referring Multi-Object Tracking
|
cs.CV cs.AI
|
Referring Multi-Object Tracking (RMOT) is a relatively new concept that has
rapidly gained traction as a promising research direction at the intersection
of computer vision and natural language processing. Unlike traditional
multi-object tracking, RMOT identifies and tracks objects and incorporates
textual descriptions for object class names, making the approach more
intuitive. Various techniques have been proposed to address this challenging
problem; however, most require the training of the entire network due to their
end-to-end nature. Among these methods, iKUN has emerged as a particularly
promising solution. Therefore, we further explore its pipeline and enhance its
performance. In this paper, we introduce a practical module dubbed
Memory-Efficient Cross-modality -- MEX. This memory-efficient technique can be
directly applied to off-the-shelf trackers like iKUN, resulting in significant
architectural improvements. Our method proves effective during inference on a
single GPU with 4 GB of memory. Among the various benchmarks, the Refer-KITTI
dataset, which offers diverse autonomous driving scenes with relevant language
expressions, is particularly useful for studying this problem. Empirically, our
method demonstrates effectiveness and efficiency regarding HOTA tracking
scores, substantially improving memory allocation and processing speed.
|
2502.13877
|
Near-Optimal List-Recovery of Linear Code Families
|
cs.IT math.CO math.IT
|
We prove several results on linear codes achieving list-recovery capacity. We
show that random linear codes achieve list-recovery capacity with constant
output list size (independent of the alphabet size and length). That is, over
alphabets of size at least $\ell^{\Omega(1/\varepsilon)}$, random linear codes
of rate $R$ are $(1-R-\varepsilon, \ell,
(\ell/\varepsilon)^{O(\ell/\varepsilon)})$-list-recoverable for all $R\in(0,1)$
and $\ell$. Together with a result of Levi, Mosheiff, and Shagrithaya, this
implies that randomly punctured Reed-Solomon codes also achieve list-recovery
capacity. We also prove that our output list size is near-optimal among all
linear codes: all $(1-R-\varepsilon, \ell, L)$-list-recoverable linear codes
must have $L\ge \ell^{\Omega(1/\varepsilon)}$.
Our simple upper bound combines the Zyablov-Pinsker argument with recent
bounds from Kopparty, Ron-Zewi, Saraf, Wootters, and Tamo on the maximum
intersection of a "list-recovery ball" and a low-dimensional subspace with
large distance. Our lower bound is inspired by a recent lower bound of Chen and
Zhang.
|
2502.13880
|
Class E/EF Inductive Power Transfer to Achieve Stable Output under
Variable Low Coupling
|
eess.SY cs.SY
|
This paper develops an inductive power transfer(IPT)system with stable output
power based on a Class E/EF inverter. Load-independent design of Class E/EF
inverter has recently attracted widespread interest. However, applying this
design to IPT systems has proven challenging when the coupling coefficient is
weak. To solve this issue, this paper uses an expanded impedance model and
substitutes the secondary side's perfect resonance with a detuned design.
Therefore, the system can maintain stable output even under a low coupling
coefficient. A 400 kHz experimental prototype validates these findings. The
experimental results indicate that the output power fluctuation remains within
15% as the coupling coefficient varies from 0.04 to 0.07. The peak power
efficiency achieving 91%
|
2502.13881
|
PSCon: Toward Conversational Product Search
|
cs.CL cs.AI cs.IR
|
Conversational Product Search (CPS) is confined to simulated conversations
due to the lack of real-world CPS datasets that reflect human-like language.
Additionally, current conversational datasets are limited to support
cross-market and multi-lingual usage. In this paper, we introduce a new CPS
data collection protocol and present PSCon, a novel CPS dataset designed to
assist product search via human-like conversations. The dataset is constructed
using a coached human-to-human data collection protocol and supports two
languages and dual markets. Also, the dataset enables thorough exploration of
six subtasks of CPS: user intent detection, keyword extraction, system action
prediction, question selection, item ranking, and response generation.
Furthermore, we also offer an analysis of the dataset and propose a benchmark
model on the proposed CPS dataset.
|
2502.13883
|
Multi-view Video-Pose Pretraining for Operating Room Surgical Activity
Recognition
|
cs.CV
|
Understanding the workflow of surgical procedures in complex operating rooms
requires a deep understanding of the interactions between clinicians and their
environment. Surgical activity recognition (SAR) is a key computer vision task
that detects activities or phases from multi-view camera recordings. Existing
SAR models often fail to account for fine-grained clinician movements and
multi-view knowledge, or they require calibrated multi-view camera setups and
advanced point-cloud processing to obtain better results. In this work, we
propose a novel calibration-free multi-view multi-modal pretraining framework
called Multiview Pretraining for Video-Pose Surgical Activity Recognition
PreViPS, which aligns 2D pose and vision embeddings across camera views. Our
model follows CLIP-style dual-encoder architecture: one encoder processes
visual features, while the other encodes human pose embeddings. To handle the
continuous 2D human pose coordinates, we introduce a tokenized discrete
representation to convert the continuous 2D pose coordinates into discrete pose
embeddings, thereby enabling efficient integration within the dual-encoder
framework. To bridge the gap between these two modalities, we propose several
pretraining objectives using cross- and in-modality geometric constraints
within the embedding space and incorporating masked pose token prediction
strategy to enhance representation learning. Extensive experiments and ablation
studies demonstrate improvements over the strong baselines, while
data-efficiency experiments on two distinct operating room datasets further
highlight the effectiveness of our approach. We highlight the benefits of our
approach for surgical activity recognition in both multi-view and single-view
settings, showcasing its practical applicability in complex surgical
environments. Code will be made available at:
https://github.com/CAMMA-public/PreViPS.
|
2502.13886
|
Refining embeddings with fill-tuning: data-efficient generalised
performance improvements for materials foundation models
|
cs.LG cs.CE
|
Pretrained foundation models learn embeddings that can be used for a wide
range of downstream tasks. These embeddings optimise general performance, and
if insufficiently accurate at a specific task the model can be fine-tuned to
improve performance. For all current methodologies this operation necessarily
degrades performance on all out-of-distribution tasks. In this work we present
'fill-tuning', a novel methodology to generate datasets for continued
pretraining of foundation models that are not suited to a particular downstream
task, but instead aim to correct poor regions of the embedding. We present the
application of roughness analysis to latent space topologies and illustrate how
it can be used to propose data that will be most valuable to improving the
embedding. We apply fill-tuning to a set of state-of-the-art materials
foundation models trained on $O(10^9)$ data points and show model improvement
of almost 1% in all downstream tasks with the addition of only 100 data points.
This method provides a route to the general improvement of foundation models at
the computational cost of fine-tuning.
|
2502.13891
|
Highly Dynamic and Flexible Spatio-Temporal Spectrum Management with
AI-Driven O-RAN: A Multi-Granularity Marketplace Framework
|
eess.SY cs.LG cs.SY
|
Current spectrum-sharing frameworks struggle with adaptability, often being
either static or insufficiently dynamic. They primarily emphasize temporal
sharing while overlooking spatial and spectral dimensions. We propose an
adaptive, AI-driven spectrum-sharing framework within the O-RAN architecture,
integrating discriminative and generative AI (GenAI) to forecast spectrum needs
across multiple timescales and spatial granularities. A marketplace model,
managed by an authorized spectrum broker, enables operators to trade spectrum
dynamically, balancing static assignments with real-time trading. GenAI
enhances traffic prediction, spectrum estimation, and allocation, optimizing
utilization while reducing costs. This modular, flexible approach fosters
operator collaboration, maximizing efficiency and revenue. A key research
challenge is refining allocation granularity and spatio-temporal dynamics
beyond existing models.
|
2502.13894
|
NavigateDiff: Visual Predictors are Zero-Shot Navigation Assistants
|
cs.RO cs.CV
|
Navigating unfamiliar environments presents significant challenges for
household robots, requiring the ability to recognize and reason about novel
decoration and layout. Existing reinforcement learning methods cannot be
directly transferred to new environments, as they typically rely on extensive
mapping and exploration, leading to time-consuming and inefficient. To address
these challenges, we try to transfer the logical knowledge and the
generalization ability of pre-trained foundation models to zero-shot
navigation. By integrating a large vision-language model with a diffusion
network, our approach named \mname ~constructs a visual predictor that
continuously predicts the agent's potential observations in the next step which
can assist robots generate robust actions. Furthermore, to adapt the temporal
property of navigation, we introduce temporal historical information to ensure
that the predicted image is aligned with the navigation scene. We then
carefully designed an information fusion framework that embeds the predicted
future frames as guidance into goal-reaching policy to solve downstream image
navigation tasks. This approach enhances navigation control and generalization
across both simulated and real-world environments. Through extensive
experimentation, we demonstrate the robustness and versatility of our method,
showcasing its potential to improve the efficiency and effectiveness of robotic
navigation in diverse settings.
|
2502.13895
|
Geometric Principles for Machine Learning of Dynamical Systems
|
cs.LG
|
Mathematical descriptions of dynamical systems are deeply rooted in
topological spaces defined by non-Euclidean geometry. This paper proposes
leveraging structure-rich geometric spaces for machine learning to achieve
structural generalization when modeling physical systems from data, in contrast
to embedding physics bias within model-free architectures. We consider model
generalization to be a function of symmetry, invariance and uniqueness, defined
as a topological mapping from state space dynamics to the parameter space. We
illustrate this view through the machine learning of linear time-invariant
dynamical systems, whose dynamics reside on the symmetric positive definite
manifold.
|
2502.13897
|
DataSciBench: An LLM Agent Benchmark for Data Science
|
cs.CL cs.AI cs.LG
|
This paper presents DataSciBench, a comprehensive benchmark for evaluating
Large Language Model (LLM) capabilities in data science. Recent related
benchmarks have primarily focused on single tasks, easily obtainable ground
truth, and straightforward evaluation metrics, which limits the scope of tasks
that can be evaluated. In contrast, DataSciBench is constructed based on a more
comprehensive and curated collection of natural and challenging prompts for
uncertain ground truth and evaluation metrics. We develop a semi-automated
pipeline for generating ground truth (GT) and validating evaluation metrics.
This pipeline utilizes and implements an LLM-based self-consistency and human
verification strategy to produce accurate GT by leveraging collected prompts,
predefined task types, and aggregate functions (metrics). Furthermore, we
propose an innovative Task - Function - Code (TFC) framework to assess each
code execution outcome based on precisely defined metrics and programmatic
rules. Our experimental framework involves testing 6 API-based models, 8
open-source general models, and 9 open-source code generation models using the
diverse set of prompts we have gathered. This approach aims to provide a more
comprehensive and rigorous evaluation of LLMs in data science, revealing their
strengths and weaknesses. Experimental results demonstrate that API-based
models outperform open-sourced models on all metrics and
Deepseek-Coder-33B-Instruct achieves the highest score among open-sourced
models. We release all code and data at https://github.com/THUDM/DataSciBench.
|
2502.13898
|
GroundCap: A Visually Grounded Image Captioning Dataset
|
cs.CV cs.CL
|
Current image captioning systems lack the ability to link descriptive text to
specific visual elements, making their outputs difficult to verify. While
recent approaches offer some grounding capabilities, they cannot track object
identities across multiple references or ground both actions and objects
simultaneously. We propose a novel ID-based grounding system that enables
consistent object reference tracking and action-object linking, and present
GroundCap, a dataset containing 52,016 images from 77 movies, with 344
human-annotated and 52,016 automatically generated captions. Each caption is
grounded on detected objects (132 classes) and actions (51 classes) using a tag
system that maintains object identity while linking actions to the
corresponding objects. Our approach features persistent object IDs for
reference tracking, explicit action-object linking, and segmentation of
background elements through K-means clustering. We propose gMETEOR, a metric
combining caption quality with grounding accuracy, and establish baseline
performance by fine-tuning Pixtral-12B. Human evaluation demonstrates our
approach's effectiveness in producing verifiable descriptions with coherent
object references.
|
2502.13899
|
AI-Driven Discovery of High Performance Polymer Electrodes for
Next-Generation Batteries
|
cond-mat.mtrl-sci cs.LG physics.app-ph
|
The use of transition group metals in electric batteries requires extensive
usage of critical elements like lithium, cobalt and nickel, which poses
significant environmental challenges. Replacing these metals with redox-active
organic materials offers a promising alternative, thereby reducing the carbon
footprint of batteries by one order of magnitude. However, this approach faces
critical obstacles, including the limited availability of suitable redox-active
organic materials and issues such as lower electronic conductivity, voltage,
specific capacity, and long-term stability. To overcome the limitations for
lower voltage and specific capacity, a machine learning (ML) driven battery
informatics framework is developed and implemented. This framework utilizes an
extensive battery dataset and advanced ML techniques to accelerate and enhance
the identification, optimization, and design of redox-active organic materials.
In this contribution, a data-fusion ML coupled meta learning model capable of
predicting the battery properties, voltage and specific capacity, for various
organic negative electrodes and charge carriers (positive electrode materials)
combinations is presented. The ML models accelerate experimentation, facilitate
the inverse design of battery materials, and identify suitable candidates from
three extensive material libraries to advance sustainable energy-storage
technologies.
|
2502.13900
|
Optimistically Optimistic Exploration for Provably Efficient
Infinite-Horizon Reinforcement and Imitation Learning
|
cs.LG
|
We study the problem of reinforcement learning in infinite-horizon discounted
linear Markov decision processes (MDPs), and propose the first computationally
efficient algorithm achieving near-optimal regret guarantees in this setting.
Our main idea is to combine two classic techniques for optimistic exploration:
additive exploration bonuses applied to the reward function, and artificial
transitions made to an absorbing state with maximal return. We show that,
combined with a regularized approximate dynamic-programming scheme, the
resulting algorithm achieves a regret of order $\tilde{\mathcal{O}} (\sqrt{d^3
(1 - \gamma)^{- 7 / 2} T})$, where $T$ is the total number of sample
transitions, $\gamma \in (0,1)$ is the discount factor, and $d$ is the feature
dimensionality. The results continue to hold against adversarial reward
sequences, enabling application of our method to the problem of imitation
learning in linear MDPs, where we achieve state-of-the-art results.
|
2502.13905
|
Partially Observable Gaussian Process Network and Doubly Stochastic
Variational Inference
|
cs.LG cs.AI
|
To reduce the curse of dimensionality for Gaussian processes (GP), they can
be decomposed into a Gaussian Process Network (GPN) of coupled subprocesses
with lower dimensionality. In some cases, intermediate observations are
available within the GPN. However, intermediate observations are often
indirect, noisy, and incomplete in most real-world systems. This work
introduces the Partially Observable Gaussian Process Network (POGPN) to model
real-world process networks. We model a joint distribution of latent functions
of subprocesses and make inferences using observations from all subprocesses.
POGPN incorporates observation lenses (observation likelihoods) into the
well-established inference method of deep Gaussian processes. We also introduce
two training methods for POPGN to make inferences on the whole network using
node observations. The application to benchmark problems demonstrates how
incorporating partial observations during training and inference can improve
the predictive performance of the overall network, offering a promising outlook
for its practical application.
|
2502.13908
|
Judging the Judges: A Collection of LLM-Generated Relevance Judgements
|
cs.IR
|
Using Large Language Models (LLMs) for relevance assessments offers promising
opportunities to improve Information Retrieval (IR), Natural Language
Processing (NLP), and related fields. Indeed, LLMs hold the promise of allowing
IR experimenters to build evaluation collections with a fraction of the manual
human labor currently required. This could help with fresh topics on which
there is still limited knowledge and could mitigate the challenges of
evaluating ranking systems in low-resource scenarios, where it is challenging
to find human annotators. Given the fast-paced recent developments in the
domain, many questions concerning LLMs as assessors are yet to be answered.
Among the aspects that require further investigation, we can list the impact of
various components in a relevance judgment generation pipeline, such as the
prompt used or the LLM chosen.
This paper benchmarks and reports on the results of a large-scale automatic
relevance judgment evaluation, the LLMJudge challenge at SIGIR 2024, where
different relevance assessment approaches were proposed. In detail, we release
and benchmark 42 LLM-generated labels of the TREC 2023 Deep Learning track
relevance judgments produced by eight international teams who participated in
the challenge. Given their diverse nature, these automatically generated
relevance judgments can help the community not only investigate systematic
biases caused by LLMs but also explore the effectiveness of ensemble models,
analyze the trade-offs between different models and human assessors, and
advance methodologies for improving automated evaluation techniques. The
released resource is available at the following link:
https://llm4eval.github.io/LLMJudge-benchmark/
|
2502.13909
|
Lost in Sequence: Do Large Language Models Understand Sequential
Recommendation?
|
cs.IR cs.AI
|
Large Language Models (LLMs) have recently emerged as promising tools for
recommendation thanks to their advanced textual understanding ability and
context-awareness. Despite the current practice of training and evaluating
LLM-based recommendation (LLM4Rec) models under a sequential recommendation
scenario, we found that whether these models understand the sequential
information inherent in users' item interaction sequences has been largely
overlooked. In this paper, we first demonstrate through a series of experiments
that existing LLM4Rec models do not fully capture sequential information both
during training and inference. Then, we propose a simple yet effective
LLM-based sequential recommender, called LLM-SRec, a method that enhances the
integration of sequential information into LLMs by distilling the user
representations extracted from a pre-trained CF-SRec model into LLMs. Our
extensive experiments show that LLM-SRec enhances LLMs' ability to understand
users' item interaction sequences, ultimately leading to improved
recommendation performance. Furthermore, unlike existing LLM4Rec models that
require fine-tuning of LLMs, LLM-SRec achieves state-of-the-art performance by
training only a few lightweight MLPs, highlighting its practicality in
real-world applications. Our code is available at
https://github.com/Sein-Kim/LLM-SRec.
|
2502.13912
|
Optimizing Research Portfolio For Semantic Impact
|
cs.IR cs.SI
|
Citation metrics are widely used to assess academic impact but suffer from
social biases, including institutional prestige and journal visibility. Here we
introduce rXiv Semantic Impact (XSI), a novel framework that predicts research
impact by analyzing how scientific semantic graphs evolve in underlying fabric
of science. Rather than counting citations, XSI tracks the evolution of
research concepts in the academic knowledge graph (KG). Starting with a
construction of a comprehensive KG from 324K biomedical publications
(2003-2025), we demonstrate that XSI can predict a paper's future semantic
impact (SI) with remarkable accuracy ($R^2$ = 0.69) three years in advance. We
leverage these predictions to develop an optimization framework for research
portfolio selection that systematically outperforms random allocation. We
propose SI as a complementary metric to citations and present XSI as a tool to
guide funding and publishing decisions, enhancing research impact while
mitigating risk.
|
2502.13913
|
How Do LLMs Perform Two-Hop Reasoning in Context?
|
cs.CL cs.AI
|
"Socrates is human. All humans are mortal. Therefore, Socrates is mortal."
This classical example demonstrates two-hop reasoning, where a conclusion
logically follows from two connected premises. While transformer-based Large
Language Models (LLMs) can make two-hop reasoning, they tend to collapse to
random guessing when faced with distracting premises. To understand the
underlying mechanism, we train a three-layer transformer on synthetic two-hop
reasoning tasks. The training dynamics show two stages: a slow learning phase,
where the 3-layer transformer performs random guessing like LLMs, followed by
an abrupt phase transitions, where the 3-layer transformer suddenly reaches
$100%$ accuracy. Through reverse engineering, we explain the inner mechanisms
for how models learn to randomly guess between distractions initially, and how
they learn to ignore distractions eventually. We further propose a
three-parameter model that supports the causal claims for the mechanisms to the
training dynamics of the transformer. Finally, experiments on LLMs suggest that
the discovered mechanisms generalize across scales. Our methodologies provide
new perspectives for scientific understandings of LLMs and our findings provide
new insights into how reasoning emerges during training.
|
2502.13915
|
Conveniently Identify Coils in Inductive Power Transfer System Using
Machine Learning
|
eess.SY cs.SY
|
High-frequency inductive power transfer (IPT) has garnered significant
attention in recent years due to its long transmission distance and high
efficiency. The inductance values L and quality factors Q of the transmitting
and receiving coils greatly influence the system's operation. Traditional
methods involved impedance analyzers or network analyzers for measurement,
which required bulky and costly equipment. Moreover, disassembling it for
re-measurement is impractical once the product is packaged. Alternatively,
simulation software such as HYSS can serve for the identification.
Nevertheless, in the case of very high frequencies, the simulation process
consumes a significant amount of time due to the skin and proximity effects.
More importantly, obtaining parameters through simulation software becomes
impractical when the coil design is more complex. This paper firstly employs a
machine learning approach for the identification task. We simply input images
of the coils and operating frequency into a well-trained model. This method
enables rapid identification of the coil's L and Q values anytime and anywhere,
without the need for expensive machinery or coil disassembly.
|
2502.13917
|
TESS 2: A Large-Scale Generalist Diffusion Language Model
|
cs.CL
|
We introduce TESS 2, a general instruction-following diffusion language model
that outperforms contemporary instruction-tuned diffusion models, as well as
matches and sometimes exceeds strong autoregressive (AR) models. We train TESS
2 by first adapting a strong AR model via continued pretraining with the usual
cross-entropy as diffusion loss, and then performing further instruction
tuning. We find that adaptation training as well as the choice of the base
model is crucial for training good instruction-following diffusion models. We
further propose reward guidance, a novel and modular inference-time guidance
procedure to align model outputs without needing to train the underlying model.
Finally, we show that TESS 2 further improves with increased inference-time
compute, highlighting the utility of diffusion LMs in having fine-grained
controllability over the amount of compute used at inference time. Code and
models are available at https://github.com/hamishivi/tess-2.
|
2502.13918
|
Playing Hex and Counter Wargames using Reinforcement Learning and
Recurrent Neural Networks
|
cs.LG
|
Hex and Counter Wargames are adversarial two-player simulations of real
military conflicts requiring complex strategic decision-making. Unlike
classical board games, these games feature intricate terrain/unit interactions,
unit stacking, large maps of varying sizes, and simultaneous move and combat
decisions involving hundreds of units. This paper introduces a novel system
designed to address the strategic complexity of Hex and Counter Wargames by
integrating cutting-edge advancements in Recurrent Neural Networks with
AlphaZero, a reliable modern Reinforcement Learning algorithm. The system
utilizes a new Neural Network architecture developed from existing research,
incorporating innovative state and action representations tailored to these
specific game environments. With minimal training, our solution has shown
promising results in typical scenarios, demonstrating the ability to generalize
across different terrain and tactical situations. Additionally, we explore the
system's potential to scale to larger map sizes. The developed system is openly
accessible, facilitating continued research and exploration within this
challenging domain.
|
2502.13920
|
Exploring Personalized Health Support through Data-Driven, Theory-Guided
LLMs: A Case Study in Sleep Health
|
cs.HC cs.CL
|
Despite the prevalence of sleep-tracking devices, many individuals struggle
to translate data into actionable improvements in sleep health. Current methods
often provide data-driven suggestions but may not be feasible and adaptive to
real-life constraints and individual contexts. We present HealthGuru, a novel
large language model-powered chatbot to enhance sleep health through
data-driven, theory-guided, and adaptive recommendations with conversational
behavior change support. HealthGuru's multi-agent framework integrates wearable
device data, contextual information, and a contextual multi-armed bandit model
to suggest tailored sleep-enhancing activities. The system facilitates natural
conversations while incorporating data-driven insights and theoretical behavior
change techniques. Our eight-week in-the-wild deployment study with 16
participants compared HealthGuru to a baseline chatbot. Results show improved
metrics like sleep duration and activity scores, higher quality responses, and
increased user motivation for behavior change with HealthGuru. We also identify
challenges and design considerations for personalization and user engagement in
health chatbots.
|
2502.13921
|
Exploring Code Language Models for Automated HLS-based Hardware
Generation: Benchmark, Infrastructure and Analysis
|
cs.LG cs.AR cs.SE
|
Recent advances in code generation have illuminated the potential of
employing large language models (LLMs) for general-purpose programming
languages such as Python and C++, opening new opportunities for automating
software development and enhancing programmer productivity. The potential of
LLMs in software programming has sparked significant interest in exploring
automated hardware generation and automation. Although preliminary endeavors
have been made to adopt LLMs in generating hardware description languages
(HDLs), several challenges persist in this direction. First, the volume of
available HDL training data is substantially smaller compared to that for
software programming languages. Second, the pre-trained LLMs, mainly tailored
for software code, tend to produce HDL designs that are more error-prone.
Third, the generation of HDL requires a significantly higher number of tokens
compared to software programming, leading to inefficiencies in cost and energy
consumption. To tackle these challenges, this paper explores leveraging LLMs to
generate High-Level Synthesis (HLS)-based hardware design. Although code
generation for domain-specific programming languages is not new in the
literature, we aim to provide experimental results, insights, benchmarks, and
evaluation infrastructure to investigate the suitability of HLS over low-level
HDLs for LLM-assisted hardware design generation. To achieve this, we first
finetune pre-trained models for HLS-based hardware generation, using a
collected dataset with text prompts and corresponding reference HLS designs. An
LLM-assisted framework is then proposed to automate end-to-end hardware code
generation, which also investigates the impact of chain-of-thought and feedback
loops promoting techniques on HLS-design generation. Limited by the timeframe
of this research, we plan to evaluate more advanced reasoning models in the
future.
|
2502.13922
|
LongPO: Long Context Self-Evolution of Large Language Models through
Short-to-Long Preference Optimization
|
cs.CL cs.LG
|
Large Language Models (LLMs) have demonstrated remarkable capabilities
through pretraining and alignment. However, superior short-context LLMs may
underperform in long-context scenarios due to insufficient long-context
alignment. This alignment process remains challenging due to the impracticality
of human annotation for extended contexts and the difficulty in balancing
short- and long-context performance. To address these challenges, we introduce
LongPO, that enables short-context LLMs to self-evolve to excel on long-context
tasks by internally transferring short-context capabilities. LongPO harnesses
LLMs to learn from self-generated short-to-long preference data, comprising
paired responses generated for identical instructions with long-context inputs
and their compressed short-context counterparts, respectively. This preference
reveals capabilities and potentials of LLMs cultivated during short-context
alignment that may be diminished in under-aligned long-context scenarios.
Additionally, LongPO incorporates a short-to-long KL constraint to mitigate
short-context performance decline during long-context alignment. When applied
to Mistral-7B-Instruct-v0.2 from 128K to 512K context lengths, LongPO fully
retains short-context performance and largely outperforms naive SFT and DPO in
both long- and short-context tasks. Specifically, LongPO-trained models can
achieve results on long-context benchmarks comparable to, or even surpassing,
those of superior LLMs (e.g., GPT-4-128K) that involve extensive long-context
annotation and larger parameter scales. Our code is available at
https://github.com/DAMO-NLP-SG/LongPO.
|
2502.13923
|
Qwen2.5-VL Technical Report
|
cs.CV cs.CL
|
We introduce Qwen2.5-VL, the latest flagship model of Qwen vision-language
series, which demonstrates significant advancements in both foundational
capabilities and innovative functionalities. Qwen2.5-VL achieves a major leap
forward in understanding and interacting with the world through enhanced visual
recognition, precise object localization, robust document parsing, and
long-video comprehension. A standout feature of Qwen2.5-VL is its ability to
localize objects using bounding boxes or points accurately. It provides robust
structured data extraction from invoices, forms, and tables, as well as
detailed analysis of charts, diagrams, and layouts. To handle complex inputs,
Qwen2.5-VL introduces dynamic resolution processing and absolute time encoding,
enabling it to process images of varying sizes and videos of extended durations
(up to hours) with second-level event localization. This allows the model to
natively perceive spatial scales and temporal dynamics without relying on
traditional normalization techniques. By training a native dynamic-resolution
Vision Transformer (ViT) from scratch and incorporating Window Attention, we
reduce computational overhead while maintaining native resolution. As a result,
Qwen2.5-VL excels not only in static image and document understanding but also
as an interactive visual agent capable of reasoning, tool usage, and task
execution in real-world scenarios such as operating computers and mobile
devices. Qwen2.5-VL is available in three sizes, addressing diverse use cases
from edge AI to high-performance computing. The flagship Qwen2.5-VL-72B model
matches state-of-the-art models like GPT-4o and Claude 3.5 Sonnet, particularly
excelling in document and diagram understanding. Additionally, Qwen2.5-VL
maintains robust linguistic performance, preserving the core language
competencies of the Qwen2.5 LLM.
|
2502.13925
|
Beyond Single Frames: Can LMMs Comprehend Temporal and Contextual
Narratives in Image Sequences?
|
cs.CL
|
Large Multimodal Models (LMMs) have achieved remarkable success across
various visual-language tasks. However, existing benchmarks predominantly focus
on single-image understanding, leaving the analysis of image sequences largely
unexplored. To address this limitation, we introduce StripCipher, a
comprehensive benchmark designed to evaluate capabilities of LMMs to comprehend
and reason over sequential images. StripCipher comprises a human-annotated
dataset and three challenging subtasks: visual narrative comprehension,
contextual frame prediction, and temporal narrative reordering. Our evaluation
of $16$ state-of-the-art LMMs, including GPT-4o and Qwen2.5VL, reveals a
significant performance gap compared to human capabilities, particularly in
tasks that require reordering shuffled sequential images. For instance, GPT-4o
achieves only 23.93% accuracy in the reordering subtask, which is 56.07% lower
than human performance. Further quantitative analysis discuss several factors,
such as input format of images, affecting the performance of LLMs in sequential
understanding, underscoring the fundamental challenges that remain in the
development of LMMs.
|
2502.13928
|
Symmetrical Visual Contrastive Optimization: Aligning Vision-Language
Models with Minimal Contrastive Images
|
cs.CV cs.AI cs.CL cs.LG
|
Recent studies have shown that Large Vision-Language Models (VLMs) tend to
neglect image content and over-rely on language-model priors, resulting in
errors in visually grounded tasks and hallucinations. We hypothesize that this
issue arises because existing VLMs are not explicitly trained to generate texts
that are accurately grounded in fine-grained image details. To enhance visual
feedback during VLM training, we propose S-VCO (Symmetrical Visual Contrastive
Optimization), a novel finetuning objective that steers the model toward
capturing important visual details and aligning them with corresponding text
tokens. To further facilitate this detailed alignment, we introduce MVC, a
paired image-text dataset built by automatically filtering and augmenting
visual counterfactual data to challenge the model with hard contrastive cases
involving Minimal Visual Contrasts. Experiments show that our method
consistently improves VLM performance across diverse benchmarks covering
various abilities and domains, achieving up to a 22% reduction in
hallucinations, and significant gains in vision-centric and general tasks.
Notably, these improvements become increasingly pronounced in benchmarks with
higher visual dependency. In short, S-VCO offers a significant enhancement of
VLM's visually-dependent task performance while retaining or even improving the
model's general abilities. We opensource our code at https://s-vco.github.io/
|
2502.13935
|
Continually Learning Structured Visual Representations via Network
Refinement with Rerelation
|
cs.CV cs.AI cs.LG
|
Current machine learning paradigm relies on continuous representations like
neural networks, which iteratively adjust parameters to approximate outcomes
rather than directly learning the structure of problem. This spreads
information across the network, causing issues like information loss and
incomprehensibility Building on prior work in environment dynamics modeling, we
propose a method that learns visual space in a structured, continual manner.
Our approach refines networks to capture the core structure of objects while
representing significant subvariants in structure efficiently. We demonstrate
this with 2D shape detection, showing incremental learning on MNIST without
overwriting knowledge and creating compact, comprehensible representations.
These results offer a promising step toward a transparent, continually learning
alternative to traditional neural networks for visual processing.
|
2502.13936
|
Image compositing is all you need for data augmentation
|
cs.CV cs.LG
|
This paper investigates the impact of various data augmentation techniques on
the performance of object detection models. Specifically, we explore classical
augmentation methods, image compositing, and advanced generative models such as
Stable Diffusion XL and ControlNet. The objective of this work is to enhance
model robustness and improve detection accuracy, particularly when working with
limited annotated data. Using YOLOv8, we fine-tune the model on a custom
dataset consisting of commercial and military aircraft, applying different
augmentation strategies. Our experiments show that image compositing offers the
highest improvement in detection performance, as measured by precision, recall,
and mean Average Precision (mAP@0.50). Other methods, including Stable
Diffusion XL and ControlNet, also demonstrate significant gains, highlighting
the potential of advanced data augmentation techniques for object detection
tasks. The results underline the importance of dataset diversity and
augmentation in achieving better generalization and performance in real-world
applications. Future work will explore the integration of semi-supervised
learning methods and further optimizations to enhance model performance across
larger and more complex datasets.
|
2502.13942
|
A Chain-of-Thought Subspace Meta-Learning for Few-shot Image Captioning
with Large Vision and Language Models
|
cs.CV
|
A large-scale vision and language model that has been pretrained on massive
data encodes visual and linguistic prior, which makes it easier to generate
images and language that are more natural and realistic. Despite this, there is
still a significant domain gap between the modalities of vision and language,
especially when training data is scarce in few-shot settings, where only very
limited data are available for training. In order to mitigate this issue, a
multi-modal meta-learning framework has been proposed to bridge the gap between
two frozen pretrained large vision and language models by introducing a tunable
prompt connecting these two large models. For few-shot image captioning, the
existing multi-model meta-learning framework utilizes a one-step prompting
scheme to accumulate the visual features of input images to guide the language
model, which struggles to generate accurate image descriptions with only a few
training samples. Instead, we propose a chain-of-thought (CoT) meta-learning
scheme as a multi-step image captioning procedure to better imitate how humans
describe images. In addition, we further propose to learn different
meta-parameters of the model corresponding to each CoT step in distinct
subspaces to avoid interference. We evaluated our method on three commonly used
image captioning datasets, i.e., MSCOCO, Flickr8k, and Flickr30k, under
few-shot settings. The results of our experiments indicate that our
chain-of-thought subspace meta-learning strategy is superior to the baselines
in terms of performance across different datasets measured by different
metrics.
|
2502.13943
|
AdaptiveStep: Automatically Dividing Reasoning Step through Model
Confidence
|
cs.AI cs.CL cs.LG
|
Current approaches for training Process Reward Models (PRMs) often involve
breaking down responses into multiple reasoning steps using rule-based
techniques, such as using predefined placeholder tokens or setting the
reasoning step's length into a fixed size. These approaches overlook the fact
that specific words do not typically mark true decision points in a text. To
address this, we propose AdaptiveStep, a method that divides reasoning steps
based on the model's confidence in predicting the next word. This division
method provides more decision-making information at each step, enhancing
downstream tasks, such as reward model learning. Moreover, our method does not
require manual annotation. We demonstrate its effectiveness through experiments
with AdaptiveStep-trained PRMs in mathematical reasoning and code generation
tasks. Experimental results indicate that the outcome PRM achieves
state-of-the-art Best-of-N performance, surpassing greedy search strategy with
token-level value-guided decoding, while also reducing construction costs by
over 30% compared to existing open-source PRMs. In addition, we provide a
thorough analysis and case study on the PRM's performance, transferability, and
generalization capabilities.
|
2502.13945
|
GPU-Friendly Laplacian Texture Blending
|
cs.GR cs.CV
|
Texture and material blending is one of the leading methods for adding
variety to rendered virtual worlds, creating composite materials, and
generating procedural content. When done naively, it can introduce either
visible seams or contrast loss, leading to an unnatural look not representative
of blended textures. Earlier work proposed addressing this problem through
careful manual parameter tuning, lengthy per-texture statistics precomputation,
look-up tables, or training deep neural networks. In this work, we propose an
alternative approach based on insights from image processing and Laplacian
pyramid blending. Our approach does not require any precomputation or increased
memory usage (other than the presence of a regular, non-Laplacian, texture
mipmap chain), does not produce ghosting, preserves sharp local features, and
can run in real time on the GPU at the cost of a few additional lower mipmap
texture taps.
|
2502.13946
|
Why Safeguarded Ships Run Aground? Aligned Large Language Models' Safety
Mechanisms Tend to Be Anchored in The Template Region
|
cs.CL cs.AI cs.CR
|
The safety alignment of large language models (LLMs) remains vulnerable, as
their initial behavior can be easily jailbroken by even relatively simple
attacks. Since infilling a fixed template between the input instruction and
initial model output is a common practice for existing LLMs, we hypothesize
that this template is a key factor behind their vulnerabilities: LLMs'
safety-related decision-making overly relies on the aggregated information from
the template region, which largely influences these models' safety behavior. We
refer to this issue as template-anchored safety alignment. In this paper, we
conduct extensive experiments and verify that template-anchored safety
alignment is widespread across various aligned LLMs. Our mechanistic analyses
demonstrate how it leads to models' susceptibility when encountering
inference-time jailbreak attacks. Furthermore, we show that detaching safety
mechanisms from the template region is promising in mitigating vulnerabilities
to jailbreak attacks. We encourage future research to develop more robust
safety alignment techniques that reduce reliance on the template region.
|
2502.13951
|
IP-Composer: Semantic Composition of Visual Concepts
|
cs.CV cs.GR
|
Content creators often draw inspiration from multiple visual sources,
combining distinct elements to craft new compositions. Modern computational
approaches now aim to emulate this fundamental creative process. Although
recent diffusion models excel at text-guided compositional synthesis, text as a
medium often lacks precise control over visual details. Image-based composition
approaches can capture more nuanced features, but existing methods are
typically limited in the range of concepts they can capture, and require
expensive training procedures or specialized data. We present IP-Composer, a
novel training-free approach for compositional image generation that leverages
multiple image references simultaneously, while using natural language to
describe the concept to be extracted from each image. Our method builds on
IP-Adapter, which synthesizes novel images conditioned on an input image's CLIP
embedding. We extend this approach to multiple visual inputs by crafting
composite embeddings, stitched from the projections of multiple input images
onto concept-specific CLIP-subspaces identified through text. Through
comprehensive evaluation, we show that our approach enables more precise
control over a larger range of visual concept compositions.
|
2502.13953
|
Neurosymbolic artificial intelligence via large language models and
coherence-driven inference
|
cs.AI
|
We devise an algorithm to generate sets of propositions that objectively
instantiate graphs that support coherence-driven inference. We then benchmark
the ability of large language models (LLMs) to reconstruct coherence graphs
from (a straightforward transformation of) propositions expressed in natural
language, with promising results from a single prompt to models optimized for
reasoning. Combining coherence-driven inference with consistency evaluations by
neural models may advance the state of the art in machine cognition.
|
2502.13954
|
Latent Distribution Decoupling: A Probabilistic Framework for
Uncertainty-Aware Multimodal Emotion Recognition
|
cs.CL cs.LG
|
Multimodal multi-label emotion recognition (MMER) aims to identify the
concurrent presence of multiple emotions in multimodal data. Existing studies
primarily focus on improving fusion strategies and modeling modality-to-label
dependencies. However, they often overlook the impact of \textbf{aleatoric
uncertainty}, which is the inherent noise in the multimodal data and hinders
the effectiveness of modality fusion by introducing ambiguity into feature
representations. To address this issue and effectively model aleatoric
uncertainty, this paper proposes Latent emotional Distribution Decomposition
with Uncertainty perception (LDDU) framework from a novel perspective of latent
emotional space probabilistic modeling. Specifically, we introduce a
contrastive disentangled distribution mechanism within the emotion space to
model the multimodal data, allowing for the extraction of semantic features and
uncertainty. Furthermore, we design an uncertainty-aware fusion multimodal
method that accounts for the dispersed distribution of uncertainty and
integrates distribution information. Experimental results show that LDDU
achieves state-of-the-art performance on the CMU-MOSEI and M$^3$ED datasets,
highlighting the importance of uncertainty modeling in MMER. Code is available
at https://github.com/201983290498/lddu\_mmer.git.
|
2502.13957
|
RAG-Gym: Optimizing Reasoning and Search Agents with Process Supervision
|
cs.CL cs.AI
|
Retrieval-augmented generation (RAG) has shown great potential for
knowledge-intensive tasks, but its traditional architectures rely on static
retrieval, limiting their effectiveness for complex questions that require
sequential information-seeking. While agentic reasoning and search offer a more
adaptive approach, most existing methods depend heavily on prompt engineering.
In this work, we introduce RAG-Gym, a unified optimization framework that
enhances information-seeking agents through fine-grained process supervision at
each search step. We also propose ReSearch, a novel agent architecture that
synergizes answer reasoning and search query generation within the RAG-Gym
framework. Experiments on four challenging datasets show that RAG-Gym improves
performance by up to 25.6\% across various agent architectures, with ReSearch
consistently outperforming existing baselines. Further analysis highlights the
effectiveness of advanced LLMs as process reward judges and the transferability
of trained reward models as verifiers for different LLMs. Additionally, we
examine the scaling properties of training and inference in agentic RAG. The
project homepage is available at https://rag-gym.github.io/.
|
2502.13959
|
LIDDIA: Language-based Intelligent Drug Discovery Agent
|
cs.CL
|
Drug discovery is a long, expensive, and complex process, relying heavily on
human medicinal chemists, who can spend years searching the vast space of
potential therapies. Recent advances in artificial intelligence for chemistry
have sought to expedite individual drug discovery tasks; however, there remains
a critical need for an intelligent agent that can navigate the drug discovery
process. Towards this end, we introduce LIDDiA, an autonomous agent capable of
intelligently navigating the drug discovery process in silico. By leveraging
the reasoning capabilities of large language models, LIDDiA serves as a
low-cost and highly-adaptable tool for autonomous drug discovery. We
comprehensively examine LIDDiA, demonstrating that (1) it can generate
molecules meeting key pharmaceutical criteria on over 70% of 30 clinically
relevant targets, (2) it intelligently balances exploration and exploitation in
the chemical space, and (3) it can identify promising novel drug candidates on
EGFR, a critical target for cancers.
|
2502.13961
|
The Computational Advantage of Depth: Learning High-Dimensional
Hierarchical Functions with Gradient Descent
|
stat.ML cs.LG
|
Understanding the advantages of deep neural networks trained by gradient
descent (GD) compared to shallow models remains an open theoretical challenge.
While the study of multi-index models with Gaussian data in high dimensions has
provided analytical insights into the benefits of GD-trained neural networks
over kernels, the role of depth in improving sample complexity and
generalization in GD-trained networks remains poorly understood. In this paper,
we introduce a class of target functions (single and multi-index Gaussian
hierarchical targets) that incorporate a hierarchy of latent subspace
dimensionalities. This framework enables us to analytically study the learning
dynamics and generalization performance of deep networks compared to shallow
ones in the high-dimensional limit. Specifically, our main theorem shows that
feature learning with GD reduces the effective dimensionality, transforming a
high-dimensional problem into a sequence of lower-dimensional ones. This
enables learning the target function with drastically less samples than with
shallow networks. While the results are proven in a controlled training
setting, we also discuss more common training procedures and argue that they
learn through the same mechanisms. These findings open the way to further
quantitative studies of the crucial role of depth in learning hierarchical
structures with deep networks.
|
2502.13962
|
Is That Your Final Answer? Test-Time Scaling Improves Selective Question
Answering
|
cs.CL
|
Scaling the test-time compute of large language models has demonstrated
impressive performance on reasoning benchmarks. However, existing evaluations
of test-time scaling make the strong assumption that a reasoning system should
always give an answer to any question provided. This overlooks concerns about
whether a model is confident in its answer, and whether it is appropriate to
always provide a response. To address these concerns, we extract confidence
scores during reasoning for thresholding model responses. We find that
increasing compute budget at inference time not only helps models answer more
questions correctly, but also increases confidence in correct responses. We
then extend the current paradigm of zero-risk responses during evaluation by
considering settings with non-zero levels of response risk, and suggest a
recipe for reporting evaluations under these settings.
|
2502.13963
|
MuDAF: Long-Context Multi-Document Attention Focusing through
Contrastive Learning on Attention Heads
|
cs.CL
|
Large Language Models (LLMs) frequently show distracted attention due to
irrelevant information in the input, which severely impairs their long-context
capabilities. Inspired by recent studies on the effectiveness of retrieval
heads in long-context factutality, we aim at addressing this distraction issue
through improving such retrieval heads directly. We propose Multi-Document
Attention Focusing (MuDAF), a novel method that explicitly optimizes the
attention distribution at the head level through contrastive learning.
According to the experimental results, MuDAF can significantly improve the
long-context question answering performance of LLMs, especially in
multi-document question answering. Extensive evaluations on retrieval scores
and attention visualizations show that MuDAF possesses great potential in
making attention heads more focused on relevant information and reducing
attention distractions.
|
2502.13964
|
A Training-Free Framework for Precise Mobile Manipulation of Small
Everyday Objects
|
cs.RO cs.AI cs.CV cs.LG
|
Many everyday mobile manipulation tasks require precise interaction with
small objects, such as grasping a knob to open a cabinet or pressing a light
switch. In this paper, we develop Servoing with Vision Models (SVM), a
closed-loop training-free framework that enables a mobile manipulator to tackle
such precise tasks involving the manipulation of small objects. SVM employs an
RGB-D wrist camera and uses visual servoing for control. Our novelty lies in
the use of state-of-the-art vision models to reliably compute 3D targets from
the wrist image for diverse tasks and under occlusion due to the end-effector.
To mitigate occlusion artifacts, we employ vision models to out-paint the
end-effector thereby significantly enhancing target localization. We
demonstrate that aided by out-painting methods, open-vocabulary object
detectors can serve as a drop-in module to identify semantic targets (e.g.
knobs) and point tracking methods can reliably track interaction sites
indicated by user clicks. This training-free method obtains an 85% zero-shot
success rate on manipulating unseen objects in novel environments in the real
world, outperforming an open-loop control method and an imitation learning
baseline trained on 1000+ demonstrations by an absolute success rate of 50%.
|
2502.13965
|
Autellix: An Efficient Serving Engine for LLM Agents as General Programs
|
cs.LG cs.AI cs.DC
|
Large language model (LLM) applications are evolving beyond simple chatbots
into dynamic, general-purpose agentic programs, which scale LLM calls and
output tokens to help AI agents reason, explore, and solve complex tasks.
However, existing LLM serving systems ignore dependencies between programs and
calls, missing significant opportunities for optimization. Our analysis reveals
that programs submitted to LLM serving engines experience long cumulative wait
times, primarily due to head-of-line blocking at both the individual LLM
request and the program. To address this, we introduce Autellix, an LLM serving
system that treats programs as first-class citizens to minimize their
end-to-end latencies. Autellix intercepts LLM calls submitted by programs,
enriching schedulers with program-level context. We propose two scheduling
algorithms-for single-threaded and distributed programs-that preempt and
prioritize LLM calls based on their programs' previously completed calls. Our
evaluation demonstrates that across diverse LLMs and agentic workloads,
Autellix improves throughput of programs by 4-15x at the same latency compared
to state-of-the-art systems, such as vLLM.
|
2502.13966
|
Where's the Bug? Attention Probing for Scalable Fault Localization
|
cs.SE cs.LG
|
Ensuring code correctness remains a challenging problem even as large
language models (LLMs) become increasingly capable at code-related tasks. While
LLM-based program repair systems can propose bug fixes using only a user's bug
report, their effectiveness is fundamentally limited by their ability to
perform fault localization (FL), a challenging problem for both humans and
LLMs. Existing FL approaches rely on executable test cases, require training on
costly and often noisy line-level annotations, or demand resource-intensive
LLMs. In this paper, we present Bug Attention Probe (BAP), a method which
learns state-of-the-art fault localization without any direct localization
labels, outperforming traditional FL baselines and prompting of large-scale
LLMs. We evaluate our approach across a variety of code settings, including
real-world Java bugs from the standard Defects4J dataset as well as seven other
datasets which span a diverse set of bug types and languages. Averaged across
all eight datasets, BAP improves by 34.6% top-1 accuracy compared to the
strongest baseline and 93.4% over zero-shot prompting GPT-4o. BAP is also
significantly more efficient than prompting, outperforming large open-weight
models at a small fraction of the computational cost.
|
2502.13967
|
FlexTok: Resampling Images into 1D Token Sequences of Flexible Length
|
cs.CV cs.LG
|
Image tokenization has enabled major advances in autoregressive image
generation by providing compressed, discrete representations that are more
efficient to process than raw pixels. While traditional approaches use 2D grid
tokenization, recent methods like TiTok have shown that 1D tokenization can
achieve high generation quality by eliminating grid redundancies. However,
these methods typically use a fixed number of tokens and thus cannot adapt to
an image's inherent complexity. We introduce FlexTok, a tokenizer that projects
2D images into variable-length, ordered 1D token sequences. For example, a
256x256 image can be resampled into anywhere from 1 to 256 discrete tokens,
hierarchically and semantically compressing its information. By training a
rectified flow model as the decoder and using nested dropout, FlexTok produces
plausible reconstructions regardless of the chosen token sequence length. We
evaluate our approach in an autoregressive generation setting using a simple
GPT-style Transformer. On ImageNet, this approach achieves an FID<2 across 8 to
128 tokens, outperforming TiTok and matching state-of-the-art methods with far
fewer tokens. We further extend the model to support to text-conditioned image
generation and examine how FlexTok relates to traditional 2D tokenization. A
key finding is that FlexTok enables next-token prediction to describe images in
a coarse-to-fine "visual vocabulary", and that the number of tokens to generate
depends on the complexity of the generation task.
|
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