id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.13290 | Prediction of Clinical Complication Onset using Neural Point Processes | cs.LG cs.AI | Predicting medical events in advance within critical care settings is
paramount for patient outcomes and resource management. Utilizing predictive
models, healthcare providers can anticipate issues such as cardiac arrest,
sepsis, or respiratory failure before they manifest. Recently, there has been a
surge in researc... |
2502.13295 | Demonstrating specification gaming in reasoning models | cs.AI | We demonstrate LLM agent specification gaming by instructing models to win
against a chess engine. We find reasoning models like o1 preview and
DeepSeek-R1 will often hack the benchmark by default, while language models
like GPT-4o and Claude 3.5 Sonnet need to be told that normal play won't work
to hack.
We improv... |
2502.13297 | Understanding and Tackling Label Errors in Individual-Level Nature
Language Understanding | cs.CL cs.AI | Natural language understanding (NLU) is a task that enables machines to
understand human language. Some tasks, such as stance detection and sentiment
analysis, are closely related to individual subjective perspectives, thus
termed individual-level NLU. Previously, these tasks are often simplified to
text-level NLU ta... |
2502.13298 | Improving Multi-turn Task Completion in Task-Oriented Dialog Systems via
Prompt Chaining and Fine-Grained Feedback | cs.CL | Task-oriented dialog (TOD) systems facilitate users in accomplishing complex,
multi-turn tasks through natural language. While traditional approaches rely on
extensive fine-tuning and annotated data for each domain, instruction-tuned
large language models (LLMs) offer a more flexible alternative. However, LLMs
strugg... |
2502.13301 | Application of Context-dependent Interpretation of Biosignals
Recognition to Control a Bionic Multifunctional Hand Prosthesis | cs.LG | The paper presents an original method for controlling a
surface-electromyography-driven (sEMG) prosthesis. A context-dependent
recognition system is proposed in which the same class of sEMG signals may have
a different interpretation, depending on the context. This allowed the
repertoire of performed movements to be ... |
2502.13308 | A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud
Detection | cs.LG | Graph fraud detection (GFD) has rapidly advanced in protecting online
services by identifying malicious fraudsters. Recent supervised GFD research
highlights that heterophilic connections between fraudsters and users can
greatly impact detection performance, since fraudsters tend to camouflage
themselves by building ... |
2502.13310 | Evaluating and Enhancing Out-of-Domain Generalization of Task-Oriented
Dialog Systems for Task Completion without Turn-level Dialog Annotations | cs.CL | Traditional task-oriented dialog (ToD) systems rely heavily on
labor-intensive turn-level annotations, such as dialogue states and policy
labels, for training. This work explores whether large language models (LLMs)
can be fine-tuned solely on natural language dialogs to perform ToD tasks,
without requiring such anno... |
2502.13311 | Training Turn-by-Turn Verifiers for Dialogue Tutoring Agents: The
Curious Case of LLMs as Your Coding Tutors | cs.CL cs.AI | Intelligent tutoring agents powered by large language models (LLMs) have been
increasingly explored to deliver personalized guidance in areas such as
language learning and science education. However, their capabilities in guiding
users to solve complex real-world tasks remain underexplored. To address this
limitation... |
2502.13313 | Revisiting Privacy, Utility, and Efficiency Trade-offs when Fine-Tuning
Large Language Models | cs.AI cs.LG | We study the inherent trade-offs in minimizing privacy risks and maximizing
utility, while maintaining high computational efficiency, when fine-tuning
large language models (LLMs). A number of recent works in privacy research have
attempted to mitigate privacy risks posed by memorizing fine-tuning data by
using diffe... |
2502.13316 | Increasing NWP Thunderstorm Predictability Using Ensemble Data and
Machine Learning | physics.ao-ph cs.LG | While numerical weather prediction (NWP) models are essential for forecasting
thunderstorms hours in advance, NWP uncertainty, which increases with lead
time, limits the predictability of thunderstorm occurrence. This study
investigates how ensemble NWP data and machine learning (ML) can enhance the
skill of thunders... |
2502.13318 | VUS: Effective and Efficient Accuracy Measures for Time-Series Anomaly
Detection | cs.LG | Anomaly detection (AD) is a fundamental task for time-series analytics with
important implications for the downstream performance of many applications. In
contrast to other domains where AD mainly focuses on point-based anomalies
(i.e., outliers in standalone observations), AD for time series is also
concerned with r... |
2502.13319 | Elucidating Mechanisms of Demographic Bias in LLMs for Healthcare | cs.CL | We know from prior work that LLMs encode social biases, and that this
manifests in clinical tasks. In this work we adopt tools from mechanistic
interpretability to unveil sociodemographic representations and biases within
LLMs in the context of healthcare. Specifically, we ask: Can we identify
activations within LLMs... |
2502.13321 | Adjust for Trust: Mitigating Trust-Induced Inappropriate Reliance on AI
Assistance | cs.HC cs.AI cs.CL | Trust biases how users rely on AI recommendations in AI-assisted
decision-making tasks, with low and high levels of trust resulting in increased
under- and over-reliance, respectively. We propose that AI assistants should
adapt their behavior through trust-adaptive interventions to mitigate such
inappropriate relianc... |
2502.13322 | Community Notes Moderate Engagement With and Diffusion of False
Information Online | cs.SI cs.CY physics.soc-ph | Social networks scaffold the diffusion of information on social media. Much
attention has been given to the spread of true vs. false content on online
social platforms, including the structural differences between their diffusion
patterns. However, much less is known about how platform interventions on false
content ... |
2502.13326 | Capturing Human Cognitive Styles with Language: Towards an Experimental
Evaluation Paradigm | cs.CL | While NLP models often seek to capture cognitive states via language, the
validity of predicted states is determined by comparing them to annotations
created without access the cognitive states of the authors. In behavioral
sciences, cognitive states are instead measured via experiments. Here, we
introduce an experim... |
2502.13328 | Observability-Blocking Controls for Double-Integrator and Higher Order
Integrator Networks | eess.SY cs.SY | The design of state-feedback controls to block observability at remote nodes
is studied for double integrator network (DIN) and higher order integrator
network models. A preliminary design algorithm is presented first for DIN that
requires $m+2$ actuation nodes to block observability for the measurement
obtained from... |
2502.13329 | Language Models Can Predict Their Own Behavior | cs.CL cs.AI cs.LG | Autoregressive Language Models output text by sequentially predicting the
next token to generate, with modern methods like Chain-of-Thought (CoT)
prompting achieving state-of-the-art reasoning capabilities by scaling the
number of generated tokens. However, are there times when we can infer how the
model will behave ... |
2502.13333 | An Uncertainty-Aware Data-Driven Predictive Controller for Hybrid Power
Plants | eess.SY cs.CE cs.SY math.OC | Given the advancements in data-driven modeling for complex engineering and
scientific applications, this work utilizes a data-driven predictive control
method, namely subspace predictive control, to coordinate hybrid power plant
components and meet a desired power demand despite the presence of weather
uncertainties.... |
2502.13335 | Geometry-Aware Diffusion Models for Multiview Scene Inpainting | cs.CV | In this paper, we focus on 3D scene inpainting, where parts of an input image
set, captured from different viewpoints, are masked out. The main challenge
lies in generating plausible image completions that are geometrically
consistent across views. Most recent work addresses this challenge by combining
generative mod... |
2502.13337 | Language Models are Few-Shot Graders | cs.CL cs.AI | Providing evaluations to student work is a critical component of effective
student learning, and automating its process can significantly reduce the
workload on human graders. Automatic Short Answer Grading (ASAG) systems,
enabled by advancements in Large Language Models (LLMs), offer a promising
solution for assessi... |
2502.13339 | How Expressive are Knowledge Graph Foundation Models? | cs.LG cs.AI | Knowledge Graph Foundation Models (KGFMs) are at the frontier for deep
learning on knowledge graphs (KGs), as they can generalize to completely novel
knowledge graphs with different relational vocabularies. Despite their
empirical success, our theoretical understanding of KGFMs remains very limited.
In this paper, we... |
2502.13342 | Beyond De-Identification: A Structured Approach for Defining and
Detecting Indirect Identifiers in Medical Texts | cs.CL | Sharing sensitive texts for scientific purposes requires appropriate
techniques to protect the privacy of patients and healthcare personnel.
Anonymizing textual data is particularly challenging due to the presence of
diverse unstructured direct and indirect identifiers. To mitigate the risk of
re-identification, this... |
2502.13344 | K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug
Interaction Prediction | cs.LG cs.CL q-bio.BM | Drug discovery is a complex and time-intensive process that requires
identifying and validating new therapeutic candidates. Computational approaches
using large-scale biomedical knowledge graphs (KGs) offer a promising solution
to accelerate this process. However, extracting meaningful insights from
large-scale KGs r... |
2502.13345 | Secure and Efficient Watermarking for Latent Diffusion Models in Model
Distribution Scenarios | cs.CR cs.AI | Latent diffusion models have exhibited considerable potential in generative
tasks. Watermarking is considered to be an alternative to safeguard the
copyright of generative models and prevent their misuse. However, in the
context of model distribution scenarios, the accessibility of models to large
scale of model user... |
2502.13347 | Craw4LLM: Efficient Web Crawling for LLM Pretraining | cs.CL | Web crawl is a main source of large language models' (LLMs) pretraining data,
but the majority of crawled web pages are discarded in pretraining due to low
data quality. This paper presents Crawl4LLM, an efficient web crawling method
that explores the web graph based on the preference of LLM pretraining.
Specifically... |
2502.13348 | System-level Analysis of Dual-Mode Networked Sensing: ISAC Integration &
Coordination Gains | cs.IT cs.SY eess.SY math.IT | This paper characterizes integration and coordination gains in dense
millimeter-wave ISAC networks through a dual-mode framework that combines
monostatic and multistatic sensing. A comprehensive system-level analysis is
conducted, accounting for base station (BS) density, power allocation, antenna
misalignment, radar... |
2502.13349 | Event Segmentation Applications in Large Language Model Enabled
Automated Recall Assessments | cs.CL | Understanding how individuals perceive and recall information in their
natural environments is critical to understanding potential failures in
perception (e.g., sensory loss) and memory (e.g., dementia). Event
segmentation, the process of identifying distinct events within dynamic
environments, is central to how we p... |
2502.13358 | Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text
Modifications | cs.CL | Large Language Models (LLMs) have transformed natural language processing,
yet they still struggle with direct text editing tasks that demand precise,
context-aware modifications. While models like ChatGPT excel in text generation
and analysis, their editing abilities often fall short, addressing only
superficial iss... |
2502.13361 | RGAR: Recurrence Generation-augmented Retrieval for Factual-aware
Medical Question Answering | cs.CL cs.AI | Medical question answering requires extensive access to specialized
conceptual knowledge. The current paradigm, Retrieval-Augmented Generation
(RAG), acquires expertise medical knowledge through large-scale corpus
retrieval and uses this knowledge to guide a general-purpose large language
model (LLM) for generating a... |
2502.13362 | Dynamic directed functional connectivity as a neural biomarker for
objective motor skill assessment | q-bio.NC cs.LG | Objective motor skill assessment plays a critical role in fields such as
surgery, where proficiency is vital for certification and patient safety.
Existing assessment methods, however, rely heavily on subjective human
judgment, which introduces bias and limits reproducibility. While recent
efforts have leveraged kine... |
2502.13363 | Pretrained Image-Text Models are Secretly Video Captioners | cs.CV cs.LG | Developing video captioning models is computationally expensive. The dynamic
nature of video also complicates the design of multimodal models that can
effectively caption these sequences. However, we find that by using minimal
computational resources and without complex modifications to address video
dynamics, an ima... |
2502.13366 | Low-Complexity Cooperative Payload Transportation for Nonholonomic
Mobile Robots Under Scalable Constraints | cs.RO cs.SY eess.SY | Cooperative transportation, a key aspect of logistics
cyber-physical systems (CPS), is typically approached using dis tributed
control and optimization-based methods. The distributed
control methods consume less time, but poorly handle and extend
to multiple constraints. Instead, optimization-based methods
ha... |
2502.13368 | A Note on Structural Controllability and Observability Indices | eess.SY cs.SY | In this note, we investigate the structural controllability and observability
indices of structured systems. We provide counter-examples showing that an
existing graph-theoretic characterization for the structural controllability
index (SCOI) may not hold, even for systems with self-loop at every state node.
We furth... |
2502.13369 | Reducing Hallucinations in Language Model-based SPARQL Query Generation
Using Post-Generation Memory Retrieval | cs.CL | The ability to generate SPARQL queries from natural language questions is
crucial for ensuring efficient and accurate retrieval of structured data from
knowledge graphs (KG). While large language models (LLMs) have been widely
adopted for SPARQL query generation, they are often susceptible to
hallucinations and out-o... |
2502.13370 | Quantum Recurrent Neural Networks with Encoder-Decoder for
Time-Dependent Partial Differential Equations | cs.LG cs.NA math.NA quant-ph | Nonlinear time-dependent partial differential equations are essential in
modeling complex phenomena across diverse fields, yet they pose significant
challenges due to their computational complexity, especially in higher
dimensions. This study explores Quantum Recurrent Neural Networks within an
encoder-decoder framew... |
2502.13372 | MoVer: Motion Verification for Motion Graphics Animations | cs.GR cs.CV | While large vision-language models can generate motion graphics animations
from text prompts, they regularly fail to include all of spatio-temporal
properties described in the prompt. We introduce MoVer, a motion verification
DSL based on first-order logic that can check spatio-temporal properties of a
motion graphic... |
2502.13373 | Fighter Jet Navigation and Combat using Deep Reinforcement Learning with
Explainable AI | cs.AI | This paper presents the development of an Artificial Intelligence (AI) based
fighter jet agent within a customized Pygame simulation environment, designed
to solve multi-objective tasks via deep reinforcement learning (DRL). The jet's
primary objectives include efficiently navigating the environment, reaching a
targe... |
2502.13374 | Task-agnostic Prompt Compression with Context-aware Sentence Embedding
and Reward-guided Task Descriptor | cs.CL | The rise of Large Language Models (LLMs) has led to significant interest in
prompt compression, a technique aimed at reducing the length of input prompts
while preserving critical information. However, the prominent approaches in
prompt compression often require explicit questions or handcrafted templates
for compres... |
2502.13376 | Learning Symbolic Task Decompositions for Multi-Agent Teams | cs.MA cs.AI cs.LG | One approach for improving sample efficiency in cooperative multi-agent
learning is to decompose overall tasks into sub-tasks that can be assigned to
individual agents. We study this problem in the context of reward machines:
symbolic tasks that can be formally decomposed into sub-tasks. In order to
handle settings w... |
2502.13383 | MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought
Verification | cs.CL cs.CV cs.LG | According to the Test-Time Scaling, the integration of External Slow-Thinking
with the Verify mechanism has been demonstrated to enhance multi-round
reasoning in large language models (LLMs). However, in the multimodal (MM)
domain, there is still a lack of a strong MM-Verifier. In this paper, we
introduce MM-Verifier... |
2502.13385 | SNN-Driven Multimodal Human Action Recognition via Event Camera and
Skeleton Data Fusion | cs.CV | Multimodal human action recognition based on RGB and skeleton data fusion,
while effective, is constrained by significant limitations such as high
computational complexity, excessive memory consumption, and substantial energy
demands, particularly when implemented with Artificial Neural Networks (ANN).
These limitati... |
2502.13388 | Reflection of Episodes: Learning to Play Game from Expert and Self
Experiences | cs.AI | StarCraft II is a complex and dynamic real-time strategy (RTS) game
environment, which is very suitable for artificial intelligence and
reinforcement learning research. To address the problem of Large Language
Model(LLM) learning in complex environments through self-reflection, we propose
a Reflection of Episodes(ROE... |
2502.13389 | Reasoning with Reinforced Functional Token Tuning | cs.AI | In this work, we propose Reinforced Functional Token Tuning (RFTT), a novel
reinforced fine-tuning framework that empowers Large Language Models (LLMs)
with self-play learn-to-reason capabilities. Unlike prior prompt-driven
reasoning efforts, RFTT embeds a rich set of learnable functional tokens (e.g.,
<analyze>, <ve... |
2502.13390 | Deep-Unfolded Massive Grant-Free Transmission in Cell-Free Wireless
Communication Systems | eess.SP cs.IT cs.LG math.IT | Grant-free transmission and cell-free communication are vital in improving
coverage and quality-of-service for massive machine-type communication. This
paper proposes a novel framework of joint active user detection, channel
estimation, and data detection (JACD) for massive grant-free transmission in
cell-free wirele... |
2502.13392 | Atomic Proximal Policy Optimization for Electric Robo-Taxi Dispatch and
Charger Allocation | cs.AI | Pioneering companies such as Waymo have deployed robo-taxi services in
several U.S. cities. These robo-taxis are electric vehicles, and their
operations require the joint optimization of ride matching, vehicle
repositioning, and charging scheduling in a stochastic environment. We model
the operations of the ride-hail... |
2502.13394 | Flow-based generative models as iterative algorithms in probability
space | cs.LG math.ST stat.ML stat.TH | Generative AI (GenAI) has revolutionized data-driven modeling by enabling the
synthesis of high-dimensional data across various applications, including image
generation, language modeling, biomedical signal processing, and anomaly
detection. Flow-based generative models provide a powerful framework for
capturing comp... |
2502.13395 | Unsupervised CP-UNet Framework for Denoising DAS Data with Decay Noise | cs.SD cs.LG eess.AS eess.SP physics.optics | Distributed acoustic sensor (DAS) technology leverages optical fiber cables
to detect acoustic signals, providing cost-effective and dense monitoring
capabilities. It offers several advantages including resistance to extreme
conditions, immunity to electromagnetic interference, and accurate detection.
However, DAS ty... |
2502.13396 | Prompting a Weighting Mechanism into LLM-as-a-Judge in Two-Step: A Case
Study | cs.CL | While Large Language Models (LLMs) have emerged as promising tools for
evaluating Natural Language Generation (NLG) tasks, their effectiveness is
limited by their inability to appropriately weigh the importance of different
topics, often overemphasizing minor details while undervaluing critical
information, leading t... |
2502.13398 | $\mathtt{GeLLM^3O}$: Generalizing Large Language Models for
Multi-property Molecule Optimization | cs.LG cs.AI cs.CL physics.chem-ph q-bio.QM | Despite recent advancements, most computational methods for molecule
optimization are constrained to single- or double-property optimization tasks
and suffer from poor scalability and generalizability to novel optimization
tasks. Meanwhile, Large Language Models (LLMs) demonstrate remarkable
out-of-domain generalizab... |
2502.13399 | MaizeEar-SAM: Zero-Shot Maize Ear Phenotyping | cs.CV | Quantifying the variation in yield component traits of maize (Zea mays L.),
which together determine the overall productivity of this globally important
crop, plays a critical role in plant genetics research, plant breeding, and the
development of improved farming practices. Grain yield per acre is calculated
by mult... |
2502.13403 | Object-Pose Estimation With Neural Population Codes | cs.RO cs.LG | Robotic assembly tasks require object-pose estimation, particularly for tasks
that avoid costly mechanical constraints. Object symmetry complicates the
direct mapping of sensory input to object rotation, as the rotation becomes
ambiguous and lacks a unique training target. Some proposed solutions involve
evaluating m... |
2502.13406 | Generative Predictive Control: Flow Matching Policies for Dynamic and
Difficult-to-Demonstrate Tasks | cs.RO cs.AI cs.SY eess.SY | Generative control policies have recently unlocked major progress in
robotics. These methods produce action sequences via diffusion or flow
matching, with training data provided by demonstrations. But despite enjoying
considerable success on difficult manipulation problems, generative policies
come with two key limit... |
2502.13407 | JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust
Multi-Teacher Knowledge Distillation Framework | cs.CV cs.AI | Deep learning has achieved significant success in the field of remote sensing
image change detection (CD), yet two major challenges remain: the scarcity of
sub-meter, all-inclusive open-source CD datasets, and the difficulty of
achieving consistent and satisfactory detection results across images with
varying change ... |
2502.13410 | Tell Me Why: Incentivizing Explanations | cs.GT cs.AI econ.TH | Common sense suggests that when individuals explain why they believe
something, we can arrive at more accurate conclusions than when they simply
state what they believe. Yet, there is no known mechanism that provides
incentives to elicit explanations for beliefs from agents. This likely stems
from the fact that stand... |
2502.13412 | Explore-Construct-Filter: An Automated Framework for Rich and Reliable
API Knowledge Graph Construction | cs.SE cs.AI | The API Knowledge Graph (API KG) is a structured network that models API
entities and their relations, providing essential semantic insights for tasks
such as API recommendation, code generation, and API misuse detection. However,
constructing a knowledge-rich and reliable API KG presents several challenges.
Existing... |
2502.13416 | Detecting LLM Fact-conflicting Hallucinations Enhanced by
Temporal-logic-based Reasoning | cs.CL | Large language models (LLMs) face the challenge of hallucinations -- outputs
that seem coherent but are actually incorrect. A particularly damaging type is
fact-conflicting hallucination (FCH), where generated content contradicts
established facts. Addressing FCH presents three main challenges: 1)
Automatically const... |
2502.13417 | RLTHF: Targeted Human Feedback for LLM Alignment | cs.CL cs.AI cs.LG | Fine-tuning large language models (LLMs) to align with user preferences is
challenging due to the high cost of quality human annotations in Reinforcement
Learning from Human Feedback (RLHF) and the generalizability limitations of AI
Feedback. To address these challenges, we propose RLTHF, a human-AI hybrid
framework ... |
2502.13418 | Empirical Study of Dynamic Regret in Online Model Predictive Control for
Linear Time-Varying Systems | eess.SY cs.SY | Model Predictive Control (MPC) is a widely used technique for managing
timevarying systems, supported by extensive theoretical analysis. While
theoretical studies employing dynamic regret frameworks have established robust
performance guarantees, their empirical validation remains sparse. This paper
investigates the ... |
2502.13420 | Probabilistically Robust Uncertainty Analysis and Optimal Control of
Continuous Lyophilization via Polynomial Chaos Theory | cs.CE cs.SY eess.SY math.OC | Lyophilization, aka freeze drying, is a process commonly used to increase the
stability of various drug products in biotherapeutics manufacturing, e.g., mRNA
vaccines, allowing for higher storage temperature. While the current trends in
the industry are moving towards continuous manufacturing, the majority of
industr... |
2502.13422 | TabSD: Large Free-Form Table Question Answering with SQL-Based Table
Decomposition | cs.CL cs.AI cs.DB | Question answering on free-form tables (TableQA) is challenging due to the
absence of predefined schemas and the presence of noise in large tables. While
Large Language Models (LLMs) have shown promise in TableQA, they struggle with
large free-form tables and noise sensitivity. To address these challenges, we
propose... |
2502.13428 | MCTS-KBQA: Monte Carlo Tree Search for Knowledge Base Question Answering | cs.CL cs.AI | This study explores how to enhance the reasoning capabilities of large
language models (LLMs) in knowledge base question answering (KBQA) by
leveraging Monte Carlo Tree Search (MCTS). Semantic parsing-based KBQA methods
are particularly challenging as these approaches require locating elements from
knowledge bases an... |
2502.13430 | Vision-Based Generic Potential Function for Policy Alignment in
Multi-Agent Reinforcement Learning | cs.AI cs.LG | Guiding the policy of multi-agent reinforcement learning to align with human
common sense is a difficult problem, largely due to the complexity of modeling
common sense as a reward, especially in complex and long-horizon multi-agent
tasks. Recent works have shown the effectiveness of reward shaping, such as
potential... |
2502.13436 | On Qualitative Preference in Alternating-time Temporal Logic with
Strategy Contexts | cs.LO cs.MA | We show how to add and eliminate binary preference on plays in
Alternating-time Temporal Logic (ATL) with strategy contexts on Concurrent Game
Models (CGMs) by means of a translation which preserves satisfaction in models
where preference-indiscernibility between plays is an equivalence relation of
finite index. The ... |
2502.13440 | Semi-supervised classification of bird vocalizations | cs.SD cs.AI cs.CV eess.AS q-bio.QM | Changes in bird populations can indicate broader changes in ecosystems,
making birds one of the most important animal groups to monitor. Combining
machine learning and passive acoustics enables continuous monitoring over
extended periods without direct human involvement. However, most existing
techniques require exte... |
2502.13441 | The Self-Improvement Paradox: Can Language Models Bootstrap Reasoning
Capabilities without External Scaffolding? | cs.CL cs.AI | Self-improving large language models (LLMs) -- i.e., to improve the
performance of an LLM by fine-tuning it with synthetic data generated by itself
-- is a promising way to advance the capabilities of LLMs while avoiding
extensive supervision. Existing approaches to self-improvement often rely on
external supervision... |
2502.13442 | TreeCut: A Synthetic Unanswerable Math Word Problem Dataset for LLM
Hallucination Evaluation | cs.CL cs.AI cs.LG | Large language models (LLMs) now achieve near-human performance on standard
math word problem benchmarks (e.g., GSM8K), yet their true reasoning ability
remains disputed. A key concern is that models often produce confident, yet
unfounded, answers to unanswerable problems. We introduce TreeCut, a synthetic
dataset th... |
2502.13443 | Physics-Aware Robotic Palletization with Online Masking Inference | cs.RO | The efficient planning of stacking boxes, especially in the online setting
where the sequence of item arrivals is unpredictable, remains a critical
challenge in modern warehouse and logistics management. Existing solutions
often address box size variations, but overlook their intrinsic and physical
properties, such a... |
2502.13446 | Adopting Whisper for Confidence Estimation | eess.AS cs.LG | Recent research on word-level confidence estimation for speech recognition
systems has primarily focused on lightweight models known as Confidence
Estimation Modules (CEMs), which rely on hand-engineered features derived from
Automatic Speech Recognition (ASR) outputs. In contrast, we propose a novel
end-to-end appro... |
2502.13447 | Enhancing Chest X-ray Classification through Knowledge Injection in
Cross-Modality Learning | cs.CV cs.CL | The integration of artificial intelligence in medical imaging has shown
tremendous potential, yet the relationship between pre-trained knowledge and
performance in cross-modality learning remains unclear. This study investigates
how explicitly injecting medical knowledge into the learning process affects
the performa... |
2502.13449 | Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular
Language Model | cs.LG physics.chem-ph | Understanding molecules is key to understanding organisms and driving
advances in drug discovery, requiring interdisciplinary knowledge across
chemistry and biology. Although large molecular language models have achieved
notable success in interpreting molecular structures, their instruction
datasets are limited to t... |
2502.13450 | Interleaved Gibbs Diffusion for Constrained Generation | cs.LG cs.AI | We introduce Interleaved Gibbs Diffusion (IGD), a novel generative modeling
framework for mixed continuous-discrete data, focusing on constrained
generation problems. Prior works on discrete and continuous-discrete diffusion
models assume factorized denoising distribution for fast generation, which can
hinder the mod... |
2502.13451 | MapNav: A Novel Memory Representation via Annotated Semantic Maps for
VLM-based Vision-and-Language Navigation | cs.RO | Vision-and-language navigation (VLN) is a key task in Embodied AI, requiring
agents to navigate diverse and unseen environments while following natural
language instructions. Traditional approaches rely heavily on historical
observations as spatio-temporal contexts for decision making, leading to
significant storage ... |
2502.13452 | Ephemerality meets LiDAR-based Lifelong Mapping | cs.RO | Lifelong mapping is crucial for the long-term deployment of robots in dynamic
environments. In this paper, we present ELite, an ephemerality-aided
LiDAR-based lifelong mapping framework which can seamlessly align multiple
session data, remove dynamic objects, and update maps in an end-to-end fashion.
Map elements are... |
2502.13457 | Provably Efficient Multi-Objective Bandit Algorithms under
Preference-Centric Customization | cs.LG | Multi-objective multi-armed bandit (MO-MAB) problems traditionally aim to
achieve Pareto optimality. However, real-world scenarios often involve users
with varying preferences across objectives, resulting in a Pareto-optimal arm
that may score high for one user but perform quite poorly for another. This
highlights th... |
2502.13458 | ThinkGuard: Deliberative Slow Thinking Leads to Cautious Guardrails | cs.CL cs.AI cs.CR cs.LG | Ensuring the safety of large language models (LLMs) is critical as they are
deployed in real-world applications. Existing guardrails rely on rule-based
filtering or single-pass classification, limiting their ability to handle
nuanced safety violations. To address this, we propose ThinkGuard, a
critique-augmented guar... |
2502.13459 | Poisoned Source Code Detection in Code Models | cs.CR cs.LG | Deep learning models have gained popularity for conducting various tasks
involving source code. However, their black-box nature raises concerns about
potential risks. One such risk is a poisoning attack, where an attacker
intentionally contaminates the training set with malicious samples to mislead
the model's predic... |
2502.13464 | Estimating Commonsense Plausibility through Semantic Shifts | cs.CL cs.AI | Commonsense plausibility estimation is critical for evaluating language
models (LMs), yet existing generative approaches--reliant on likelihoods or
verbalized judgments--struggle with fine-grained discrimination. In this paper,
we propose ComPaSS, a novel discriminative framework that quantifies
commonsense plausibil... |
2502.13465 | HawkBench: Investigating Resilience of RAG Methods on Stratified
Information-Seeking Tasks | cs.IR cs.AI cs.CL | In real-world information-seeking scenarios, users have dynamic and diverse
needs, requiring RAG systems to demonstrate adaptable resilience. To
comprehensively evaluate the resilience of current RAG methods, we introduce
HawkBench, a human-labeled, multi-domain benchmark designed to rigorously
assess RAG performance... |
2502.13467 | Continuous K-Max Bandits | cs.LG | We study the $K$-Max combinatorial multi-armed bandits problem with
continuous outcome distributions and weak value-index feedback: each base arm
has an unknown continuous outcome distribution, and in each round the learning
agent selects $K$ arms, obtains the maximum value sampled from these $K$ arms
as reward and o... |
2502.13471 | Some Insights of Construction of Feature Graph to Learn Pairwise Feature
Interactions with Graph Neural Networks | cs.LG cs.AI stat.ML | Feature interaction is crucial in predictive machine learning models, as it
captures the relationships between features that influence model performance.
In this work, we focus on pairwise interactions and investigate their
importance in constructing feature graphs for Graph Neural Networks (GNNs).
Rather than propos... |
2502.13472 | FlexDuo: A Pluggable System for Enabling Full-Duplex Capabilities in
Speech Dialogue Systems | cs.CL cs.HC | Full-Duplex Speech Dialogue Systems (Full-Duplex SDS) have significantly
enhanced the naturalness of human-machine interaction by enabling real-time
bidirectional communication. However, existing approaches face challenges such
as difficulties in independent module optimization and contextual noise
interference due t... |
2502.13474 | Towards Lightweight, Adaptive and Attribute-Aware Multi-Aspect
Controllable Text Generation with Large Language Models | cs.CL | Multi-aspect controllable text generation aims to control text generation in
attributes from multiple aspects, making it a complex but powerful task in
natural language processing. Supervised fine-tuning methods are often employed
for this task due to their simplicity and effectiveness. However, they still
have some ... |
2502.13475 | LLM should think and action as a human | cs.CL cs.AI | It is popular lately to train large language models to be used as chat
assistants, but in the conversation between the user and the chat assistant,
there are prompts, require multi-turns between the chat assistant and the user.
However, there are a number of issues with the multi-turns conversation: The
response of t... |
2502.13476 | Integration of Agentic AI with 6G Networks for Mission-Critical
Applications: Use-case and Challenges | cs.AI cs.NI | We are in a transformative era, and advances in Artificial Intelligence (AI),
especially the foundational models, are constantly in the news. AI has been an
integral part of many applications that rely on automation for service
delivery, and one of them is mission-critical public safety applications. The
problem with... |
2502.13477 | An Enhancement of Cuckoo Search Algorithm for Optimal Earthquake
Evacuation Space Allocation in Intramuros, Manila City | cs.NE | The Cuckoo Search Algorithm (CSA), while effective in solving complex
optimization problems, faces limitations in random population initialization
and reliance on fixed parameters. Random initialization of the population often
results in clustered solutions, resulting in uneven exploration of the search
space and hin... |
2502.13480 | Astra: Efficient and Money-saving Automatic Parallel Strategies Search
on Heterogeneous GPUs | cs.DC cs.AI | In this paper, we introduce an efficient and money-saving automatic parallel
strategies search framework on heterogeneous GPUs: Astra. First, Astra searches
for the efficiency-optimal parallel strategy in both GPU configurations search
space (GPU types and GPU numbers) and parallel parameters search space. Then,
Astr... |
2502.13481 | LLM4Tag: Automatic Tagging System for Information Retrieval via Large
Language Models | cs.IR | Tagging systems play an essential role in various information retrieval
applications such as search engines and recommender systems. Recently, Large
Language Models (LLMs) have been applied in tagging systems due to their
extensive world knowledge, semantic understanding, and reasoning capabilities.
Despite achieving... |
2502.13482 | Smoothed Normalization for Efficient Distributed Private Optimization | cs.LG cs.CR cs.DC math.OC stat.ML | Federated learning enables training machine learning models while preserving
the privacy of participants. Surprisingly, there is no differentially private
distributed method for smooth, non-convex optimization problems. The reason is
that standard privacy techniques require bounding the participants'
contributions, u... |
2502.13484 | 2.5D U-Net with Depth Reduction for 3D CryoET Object Identification | cs.CV | Cryo-electron tomography (cryoET) is a crucial technique for unveiling the
structure of protein complexes. Automatically analyzing tomograms captured by
cryoET is an essential step toward understanding cellular structures. In this
paper, we introduce the 4th place solution from the CZII - CryoET Object
Identification... |
2502.13486 | Kernel Mean Embedding Topology: Weak and Strong Forms for Stochastic
Kernels and Implications for Model Learning | eess.SY cs.LG cs.SY math.OC math.ST stat.TH | We introduce a novel topology, called Kernel Mean Embedding Topology, for
stochastic kernels, in a weak and strong form. This topology, defined on the
spaces of Bochner integrable functions from a signal space to a space of
probability measures endowed with a Hilbert space structure, allows for a
versatile formulatio... |
2502.13487 | Transferring Textual Preferences to Vision-Language Understanding
through Model Merging | cs.CL cs.AI cs.CV cs.LG | Large vision-language models (LVLMs) perform outstandingly across various
multimodal tasks. However, their ability to evaluate generated content remains
limited, and training vision-language reward models (VLRMs) with preference
data is computationally expensive. This paper explores a training-free
alternative by mer... |
2502.13490 | What are Models Thinking about? Understanding Large Language Model
Hallucinations "Psychology" through Model Inner State Analysis | cs.CL cs.AI | Large language model (LLM) systems suffer from the models' unstable ability
to generate valid and factual content, resulting in hallucination generation.
Current hallucination detection methods heavily rely on out-of-model
information sources, such as RAG to assist the detection, thus bringing heavy
additional latenc... |
2502.13495 | A Study on Monthly Marine Heatwave Forecasts in New Zealand: An
Investigation of Imbalanced Regression Loss Functions with Neural Network
Models | physics.ao-ph cs.LG stat.AP | Marine heatwaves (MHWs) are extreme ocean-temperature events with significant
impacts on marine ecosystems and related industries. Accurate forecasts (one to
six months ahead) of MHWs would aid in mitigating these impacts. However,
forecasting MHWs presents a challenging imbalanced regression task due to the
rarity o... |
2502.13497 | Towards Geo-Culturally Grounded LLM Generations | cs.CL cs.AI | Generative large language models (LLMs) have been demonstrated to have gaps
in diverse, cultural knowledge across the globe. We investigate the effect of
retrieval augmented generation and search-grounding techniques on the ability
of LLMs to display familiarity with a diverse range of national cultures.
Specifically... |
2502.13498 | Improving Collision-Free Success Rate For Object Goal Visual Navigation
Via Two-Stage Training With Collision Prediction | cs.RO cs.CV | The object goal visual navigation is the task of navigating to a specific
target object using egocentric visual observations. Recent end-to-end
navigation models based on deep reinforcement learning have achieved remarkable
performance in finding and reaching target objects. However, the collision
problem of these mo... |
2502.13499 | Hidden Darkness in LLM-Generated Designs: Exploring Dark Patterns in
Ecommerce Web Components Generated by LLMs | cs.HC cs.AI cs.LG | Recent work has highlighted the risks of LLM-generated content for a wide
range of harmful behaviors, including incorrect and harmful code. In this work,
we extend this by studying whether LLM-generated web design contains dark
patterns. This work evaluated designs of ecommerce web components generated by
four popula... |
2502.13502 | PLDR-LLMs Learn A Generalizable Tensor Operator That Can Replace Its Own
Deep Neural Net At Inference | cs.CL cs.AI cs.LG | We show that Large Language Model from Power Law Decoder Representations
(PLDR-LLM) is a foundational model whose deductive outputs are invariant
tensors up to a small perturbation. PLDR-LLM learns a singularity condition for
the deductive outputs that enable the once-inferred energy-curvature tensor
$\mathbf{G}_{LM}... |
2502.13506 | Reproducing NevIR: Negation in Neural Information Retrieval | cs.IR | Negation is a fundamental aspect of human communication, yet it remains a
challenge for Language Models (LMs) in Information Retrieval (IR). Despite the
heavy reliance of modern neural IR systems on LMs, little attention has been
given to their handling of negation. In this study, we reproduce and extend the
findings... |
2502.13508 | VLAS: Vision-Language-Action Model With Speech Instructions For
Customized Robot Manipulation | cs.RO | Vision-language-action models (VLAs) have become increasingly popular in
robot manipulation for their end-to-end design and remarkable performance.
However, existing VLAs rely heavily on vision-language models (VLMs) that only
support text-based instructions, neglecting the more natural speech modality
for human-robo... |
2502.13509 | Unlocking Multimodal Integration in EHRs: A Prompt Learning Framework
for Language and Time Series Fusion | cs.CL cs.AI cs.LG | Large language models (LLMs) have shown remarkable performance in
vision-language tasks, but their application in the medical field remains
underexplored, particularly for integrating structured time series data with
unstructured clinical notes. In clinical practice, dynamic time series data
such as lab test results ... |
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