paper_id stringlengths 10 10 | title stringlengths 6 214 | content stringlengths 5.3k 106k | metadata stringlengths 864 1.75k | label stringclasses 2
values | references listlengths 1 4 |
|---|---|---|---|---|---|
2511.08364 | DPRM: A Dual Implicit Process Reward Model in Multi-Hop Question Answering |
# DPRM: A DUAL IMPLICIT PROCESS REWARD MODEL IN MULTI-HOP QUESTION ANSWERING
Anonymous Submission
Anonymous Institute
# ABSTRACT
In multi-hop question answering (MHQA) tasks, Chain of Thought (CoT) improves the quality of generation by guiding large language models (LLMs) through multi-step reasoning, and Knowled... | {"arxiv_id": "2511.08364", "answer": "Accept", "venue": "aaai", "conference_year": 2026, "arxiv_year": 2025, "decision": "accept", "training_label": "accept", "title": "DPRM: A Dual Implicit Process Reward Model in Multi-Hop Question Answering", "submission_date": "2025-11-11", "body_pages": 7.0, "anonymization_verifie... | Accept | [
[
"arxiv-mini",
"test"
],
[
"arxiv-mini",
"test_yup"
],
[
"arxiv",
"test"
],
[
"arxiv",
"test_yup"
]
] |
2411.12877 | The Illusion of Empathy: How AI Chatbots Shape Conversation Perception |
# THE ILLUSION OF EMPATHY: HOW AI CHATBOTS SHAPE CONVERSATION PERCEPTION
Anonymous Submission
Anonymous Institute
# ABSTRACT
As AI chatbots increasingly incorporate empathy, understanding user-centered perceptions of chatbot empathy and its impact on conversation quality remains essential yet underexplored. This ... | {"arxiv_id": "2411.12877", "answer": "Accept", "venue": "aaai", "conference_year": 2025, "arxiv_year": 2024, "decision": "accept", "training_label": "accept", "citations": 1, "pct_citation": 0.6699, "title": "The Illusion of Empathy: How AI Chatbots Shape Conversation Perception", "submission_date": "2024-11-19", "body... | Accept | [
[
"arxiv-mini",
"test"
],
[
"arxiv-mini",
"test_yup"
],
[
"arxiv",
"test"
],
[
"arxiv",
"test_yup"
]
] |
2511.17910 | L2V-CoT: Cross-Modal Transfer of Chain-of-Thought Reasoning via Latent Intervention |
# L2V-COT: CROSS-MODAL TRANSFER OF CHAIN-OF-THOUGHT REASONING VIA LATENT INTERVENTION
Anonymous Submission
Anonymous Institute
# ABSTRACT
Recently, Chain-of-Thought (CoT) reasoning has significantly enhanced the capabilities of large language models (LLMs), but Vision-Language Models (VLMs) still struggle with mu... | {"arxiv_id": "2511.17910", "answer": "Accept", "venue": "aaai", "conference_year": 2026, "arxiv_year": 2025, "decision": "accept", "training_label": "accept", "title": "L2V-CoT: Cross-Modal Transfer of Chain-of-Thought Reasoning via Latent Intervention", "submission_date": "2025-11-22", "body_pages": 6.0, "anonymizatio... | Accept | [
[
"arxiv-mini",
"test"
],
[
"arxiv-mini",
"test_yup"
],
[
"arxiv",
"test"
],
[
"arxiv",
"test_yup"
]
] |
2511.12075 | Treatment Stitching with Schr\"odinger Bridge for Enhancing Offline Reinforcement Learning in Adaptive Treatment Strategies |
# TREATMENT STITCHING WITH SCHRÖDINGER BRIDGE FOR ENHANCING OFFLINE REINFORCEMENT LEARNING IN ADAPTIVE TREATMENT STRATEGIES
Anonymous Submission
Anonymous Institute
# ABSTRACT
Adaptive treatment strategies (ATS) are sequential decision-making processes that enable personalized care by dynamically adjusting treatm... | {"arxiv_id": "2511.12075", "answer": "Accept", "venue": "aaai", "conference_year": 2026, "arxiv_year": 2025, "decision": "accept", "training_label": "accept", "title": "Treatment Stitching with Schr\\\"odinger Bridge for Enhancing Offline Reinforcement Learning in Adaptive Treatment Strategies", "submission_date": "202... | Accept | [
[
"arxiv-mini",
"test"
],
[
"arxiv-mini",
"test_yup"
],
[
"arxiv",
"test"
],
[
"arxiv",
"test_yup"
]
] |
2602.03615 | KTV: Keyframes and Key Tokens Selection for Efficient Training-Free Video LLMs |
# KTV: KEYFRAMES AND KEY TOKENS SELECTION FOR EFFICIENT TRAINING-FREE VIDEO LLMS
Anonymous Submission
Anonymous Institute
# ABSTRACT
Training-free video understanding leverages the strong image comprehension capabilities of pre-trained vision language models (VLMs) by treating a video as a sequence of static fram... | {"arxiv_id": "2602.03615", "answer": "Accept", "venue": "aaai", "conference_year": 2026, "arxiv_year": 2026, "decision": "accept", "training_label": "accept", "title": "KTV: Keyframes and Key Tokens Selection for Efficient Training-Free Video LLMs", "submission_date": "2026-02-03", "body_pages": 6.0, "anonymization_ver... | Accept | [
[
"arxiv-mini",
"test"
],
[
"arxiv-mini",
"test_yup"
],
[
"arxiv",
"test"
],
[
"arxiv",
"test_yup"
]
] |
2412.10712 | Towards Effective, Efficient and Unsupervised Social Event Detection in
the Hyperbolic Space | "\n\n# TOWARDS EFFECTIVE, EFFICIENT AND UNSUPERVISED SOCIAL EVENT DETECTION IN THE HYPERBOLIC SPACE\(...TRUNCATED) | "{\"arxiv_id\": \"2412.10712\", \"answer\": \"Accept\", \"venue\": \"aaai\", \"conference_year\": 20(...TRUNCATED) | Accept | [
[
"arxiv-mini",
"test"
],
[
"arxiv-mini",
"test_yup"
],
[
"arxiv",
"test"
],
[
"arxiv",
"test_yup"
]
] |
2002.03082 | RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement
Learning | "\n\n# RL-DUET: ONLINE MUSIC ACCOMPANIMENT GENERATION USING DEEP REINFORCEMENT LEARNING\n\nAnonymous(...TRUNCATED) | "{\"arxiv_id\": \"2002.03082\", \"answer\": \"Accept\", \"venue\": \"aaai\", \"conference_year\": 20(...TRUNCATED) | Accept | [
[
"arxiv-mini",
"test"
],
[
"arxiv",
"test"
]
] |
2412.18844 | "Improving Integrated Gradient-based Transferable Adversarial Examples by\n Refining the Integratio(...TRUNCATED) | "\n\n# IMPROVING INTEGRATED GRADIENT-BASED TRANSFERABLE ADVERSARIAL EXAMPLES BY REFINING THE INTEGRA(...TRUNCATED) | "{\"arxiv_id\": \"2412.18844\", \"answer\": \"Accept\", \"venue\": \"aaai\", \"conference_year\": 20(...TRUNCATED) | Accept | [
[
"arxiv-mini",
"test"
],
[
"arxiv-mini",
"test_yup"
],
[
"arxiv",
"test"
],
[
"arxiv",
"test_yup"
]
] |
2203.02172 | Semantic-Aware Representation Blending for Multi-Label Image Recognition
with Partial Labels | "\n\n# SEMANTIC-AWARE REPRESENTATION BLENDING FOR MULTI-LABEL IMAGE RECOGNITION WITH PARTIAL LABELS\(...TRUNCATED) | "{\"arxiv_id\": \"2203.02172\", \"answer\": \"Accept\", \"venue\": \"aaai\", \"conference_year\": 20(...TRUNCATED) | Accept | [
[
"arxiv-mini",
"test"
],
[
"arxiv",
"test"
]
] |
2511.11693 | Value-Aligned Prompt Moderation via Zero-Shot Agentic Rewriting for Safe Image Generation | "\n\n# VALUE-ALIGNED PROMPT MODERATION VIA ZERO-SHOT AGENTIC REWRITING FOR SAFE IMAGE GENERATION\n\n(...TRUNCATED) | "{\"arxiv_id\": \"2511.11693\", \"answer\": \"Accept\", \"venue\": \"aaai\", \"conference_year\": 20(...TRUNCATED) | Accept | [
[
"arxiv-mini",
"test"
],
[
"arxiv-mini",
"test_yup"
],
[
"arxiv",
"test"
],
[
"arxiv",
"test_yup"
]
] |
End of preview. Expand in Data Studio
pl-text-nips
Text version of the OpenReview-ICLR and arXiv PaperLens datasets. Anonymous release for double-blind review.
Each row is one unique paper. We release all extracted papers — not every
paper here is used in our downstream training/eval sets. The papers that are
used are denoted by the references field, which lists every internal
(release_name, release_split) pair the paper belongs to (a single paper can
belong to multiple). The accompanying reconstruction.py in the anonymous code
release reads this field to materialize the original sharegpt data.json for
any of the publishable text keys.
Configs (subsets)
arxiv— papers from arXiv (per_venue + 21k families + residual + the arXiv side of combined).openreview-iclr— papers from ICLR via OpenReview (balanced_original + max_rejects + train_50pct/75pct + the iclr side of combined).
from datasets import load_dataset
ds_arxiv = load_dataset("anonuser231357/pl-text-nips", "arxiv", split="papers")
ds_iclr = load_dataset("anonuser231357/pl-text-nips", "openreview-iclr", split="papers")
Schema
| field | type | description |
|---|---|---|
paper_id |
string |
arXiv id or OpenReview submission id |
title |
string |
paper title |
content |
string |
prompt-stripped body (the full paper body in markdown) |
metadata |
string |
JSON blob — venue, year, authors, ratings, decision, … |
label |
string |
"Accept" or "Reject" |
references |
list<list<string>> |
each entry is [release_name, release_split] — the internal splits this paper belongs to |
Reconstructing the sharegpt data.json files
reconstruction.py (in the anonymous code release) rebuilds any of the
publishable internal keys (e.g. arxiv_50_50_21k_text_..._y24up_test)
byte-identically from this dataset. Point --hf_text_repo at this repo:
python scripts/reconstruction.py \
--hf_text_repo anonuser231357/pl-text-nips \
--dataset_keys arxiv_50_50_balanced_per_venue_text_wmetadata_filtered24480_train
Reconstructed files land in ./data/ by default (override with --data_root <path>):
data/<dataset_key>/data.json (sharegpt rows) and data/dataset_info.json
(LlamaFactory entry).
The release ships a manifest.json sidecar mapping each internal
dataset_info.json key → (release_name, release_split, columns, file_name),
so reconstruction reproduces conversations, _metadata, and
accept_reject_label (where applicable).
License & citation
License: other. Citation withheld for anonymous review.
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