Orionnnn/hub-stats huggingface dataset:
The Personalization Trap: How User Memory Alters Emotional Reasoning in
  LLMs
Authors: name: Xi Fang, name: Weijie Xu, name: Yuchong Zhang, name: Stephanie Eckman, name: Scott Nickleach, name: Chandan K. Reddy
Published: 2025-10-10 22:39:37

When an AI assistant remembers that Sarah is a single mother working two
jobs, does it interpret her stress differently than if she were a wealthy
executive? As personalized AI systems increasingly incorporate long-term user
memory, understanding how this memory shapes emotional reasoning is critical.
We investigate how user memory affects emotional intelligence in large language
models (LLMs) by evaluating 15 models on human validated emotional intelligence
tests. We find that identical scenarios paired with different user profiles
produce systematically divergent emotional interpretations. Across validated
user independent emotional scenarios and diverse user profiles, systematic
biases emerged in several high-performing LLMs where advantaged profiles
received more accurate emotional interpretations. Moreover, LLMs demonstrate
significant disparities across demographic factors in emotion understanding and
supportive recommendations tasks, indicating that personalization mechanisms
can embed social hierarchies into models emotional reasoning. These results
highlight a key challenge for memory enhanced AI: systems designed for
personalization may inadvertently reinforce social inequalities.
QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning
  for LLMs
Authors: name: Wei Huang, name: Yi Ge, name: Shuai Yang, name: Yicheng Xiao, name: Huizi Mao, name: Yujun Lin, name: Hanrong Ye, name: Sifei Liu, name: Ka Chun Cheung, name: Hongxu Yin, name: Yao Lu, name: Xiaojuan Qi, name: Song Han, name: Yukang Chen
Published: 2025-10-13 17:55:09

We propose QeRL, a Quantization-enhanced Reinforcement Learning framework for
large language models (LLMs). While RL is essential for LLMs' reasoning
capabilities, it is resource-intensive, requiring substantial GPU memory and
long rollout durations. QeRL addresses these issues by combining NVFP4
quantization with Low-Rank Adaptation (LoRA), accelerating rollout phase of RL
while reducing memory overhead. Beyond efficiency, our findings show that
quantization noise increases policy entropy, enhancing exploration, and
enabling the discovery of better strategies during RL. To further optimize
exploration, QeRL introduces an Adaptive Quantization Noise (AQN) mechanism,
which dynamically adjusts noise during training. Experiments demonstrate that
QeRL delivers over 1.5 times speedup in the rollout phase. Moreover, this is
the first framework to enable RL training of a 32B LLM on a single H100 80GB
GPU, while delivering overall speedups for RL training. It also achieves faster
reward growth and higher final accuracy than 16-bit LoRA and QLoRA, while
matching the performance of full-parameter fine-tuning on mathematical
benchmarks such as GSM8K (90.8%) and MATH 500 (77.4%) in the 7B model. These
results establish QeRL as an efficient and effective framework for RL training
in LLMs.
LLaMAX2: Your Translation-Enhanced Model also Performs Well in Reasoning
Authors: name: Changjiang Gao, name: Zixian Huang, name: Jingyang Gong, name: Shujian Huang, name: Lei Li, name: Fei Yuan
Published: 2025-10-10 09:33:28

General Large Language Models (LLMs) excel in reasoning, but those enhanced
for translation struggle with reasoning tasks. To address this, we propose a
novel translationenhanced recipe that begins with instruct models and applies
layer-selective tuning only on parallel data. Following this pipeline, we
introduce the Qwen3-XPlus models, which demonstrate significant improvements in
translation performance across both high- and lowresource languages, achieving
15+ spBLEU and 40+ xComet in low-resource languages, like Swahili.
Interestingly, training only with small parallel datasets, Qwen3-XPlus achieves
an average improvement of 1+ points on 7 multilingual tasks while maintaining
proficiency comparable to the Qwen3 instruct model in 15 popular reasoning
datasets. This work offers a promising approach to multilingual enhancement,
significantly reducing complexity and enhancing accessibility for a wider range
of languages. The code and model are publicly available.
Demystifying Reinforcement Learning in Agentic Reasoning
Authors: name: Zhaochen Yu, name: Ling Yang, name: Jiaru Zou, name: Shuicheng Yan, name: Mengdi Wang
Published: 2025-10-13 17:57:15

Recently, the emergence of agentic RL has showcased that RL could also
effectively improve the agentic reasoning ability of LLMs, yet the key design
principles and optimal practices remain unclear. In this work, we conduct a
comprehensive and systematic investigation to demystify reinforcement learning
in agentic reasoning from three key perspectives: data, algorithm, and
reasoning mode. We highlight our key insights: (i) Replacing stitched synthetic
trajectories with real end-to-end tool-use trajectories yields a far stronger
SFT initialization; high-diversity, model-aware datasets sustain exploration
and markedly improve RL performance. (ii) Exploration-friendly techniques are
crucial for agentic RL, such as clip higher, overlong reward shaping, and
maintaining adequate policy entropy could improve the training efficiency.
(iii) A deliberative strategy with fewer tool calls outperforms frequent tool
calls or verbose self-reasoning, improving tool efficiency and final accuracy.
Together, these simple practices consistently enhance agentic reasoning and
training efficiency, achieving strong results on challenging benchmarks with
smaller models, and establishing a practical baseline for future agentic RL
research. Beyond these empirical insights, we further contribute a
high-quality, real end-to-end agentic SFT dataset along with a high-quality RL
dataset, and demonstrate the effectiveness of our insights in boosting the
agentic reasoning ability of LLMs across four challenging benchmarks, including
AIME2024/AIME2025, GPQA-Diamond, and LiveCodeBench-v6. With our recipes,
4B-sized models could also achieve superior agentic reasoning performance
compared to 32B-sized models. Code and models:
https://github.com/Gen-Verse/Open-AgentRL
Skill-Targeted Adaptive Training
Authors: name: Yinghui He, name: Abhishek Panigrahi, name: Yong Lin, name: Sanjeev Arora
Published: 2025-10-11 05:02:36

Language models often show little to no improvement (i.e., "saturation") when
trained via vanilla supervised fine-tuning (SFT) on data similar to what they
saw in their training set (e.g., MATH). We introduce a new fine-tuning
strategy, STAT, to train such a student model by using the metacognition
ability of a stronger large language model (LLM) as the teacher. The teacher
uses the task dataset to create a list of skills needed for the task, and then
labels each data point with its required skills (Didolkar et al., 2024). By
monitoring the student's answers, the teacher creates a Missing-Skill-Profile
for the student, tracking how often they failed to apply each skill in their
responses. We use this idea to build a modified training set in one of two
ways. In STAT-Sel, the teacher uses an existing set of training examples but
adaptively reweights them according to the Missing-Skill-Profile. In STAT-Syn,
the teacher synthesizes additional examples involving missing skills. Across
extensive experiments on Llama and Qwen models, our methods yield improvements
of up to 7.5% on MATH, whereas SFT provides only limited gains. Furthermore,
STAT enhances performance on out-of-distribution benchmarks (e.g., AIME24/25,
AMC23, etc.) by an average of 4.6%. Crucially, we find that STAT is
complementary to RL via GRPO (Shao et al., 2024): after the model is improved
using STAT to address skill gaps, GRPO continues to add further gains. We
conclude that skill-targeted adaptive training should broadly improve current
training pipelines. Our code is available at:
https://github.com/princeton-pli/STAT.
Graph Diffusion Transformers are In-Context Molecular Designers
Authors: name: Gang Liu, name: Jie Chen, name: Yihan Zhu, name: Michael Sun, name: Tengfei Luo, name: Nitesh V Chawla, name: Meng Jiang
Published: 2025-10-09 18:56:57

In-context learning allows large models to adapt to new tasks from a few
demonstrations, but it has shown limited success in molecular design. Existing
databases such as ChEMBL contain molecular properties spanning millions of
biological assays, yet labeled data for each property remain scarce. To address
this limitation, we introduce demonstration-conditioned diffusion models
(DemoDiff), which define task contexts using a small set of molecule-score
examples instead of text descriptions. These demonstrations guide a denoising
Transformer to generate molecules aligned with target properties. For scalable
pretraining, we develop a new molecular tokenizer with Node Pair Encoding that
represents molecules at the motif level, requiring 5.5times fewer nodes. We
curate a dataset containing millions of context tasks from multiple sources
covering both drugs and materials, and pretrain a 0.7-billion-parameter model
on it. Across 33 design tasks in six categories, DemoDiff matches or surpasses
language models 100-1000times larger and achieves an average rank of 3.63
compared to 5.25-10.20 for domain-specific approaches. These results position
DemoDiff as a molecular foundation model for in-context molecular design. Our
code is available at https://github.com/liugangcode/DemoDiff.
SPG: Sandwiched Policy Gradient for Masked Diffusion Language Models
Authors: name: Chenyu Wang, name: Paria Rashidinejad, name: DiJia Su, name: Song Jiang, name: Sid Wang, name: Siyan Zhao, name: Cai Zhou, name: Shannon Zejiang Shen, name: Feiyu Chen, name: Tommi Jaakkola, name: Yuandong Tian, name: Bo Liu
Published: 2025-10-10 16:52:25

Diffusion large language models (dLLMs) are emerging as an efficient
alternative to autoregressive models due to their ability to decode multiple
tokens in parallel. However, aligning dLLMs with human preferences or
task-specific rewards via reinforcement learning (RL) is challenging because
their intractable log-likelihood precludes the direct application of standard
policy gradient methods. While prior work uses surrogates like the evidence
lower bound (ELBO), these one-sided approximations can introduce significant
policy gradient bias. To address this, we propose the Sandwiched Policy
Gradient (SPG) that leverages both an upper and a lower bound of the true
log-likelihood. Experiments show that SPG significantly outperforms baselines
based on ELBO or one-step estimation. Specifically, SPG improves the accuracy
over state-of-the-art RL methods for dLLMs by 3.6% in GSM8K, 2.6% in MATH500,
18.4% in Countdown and 27.0% in Sudoku.
DocReward: A Document Reward Model for Structuring and Stylizing
Authors: name: Junpeng Liu, name: Yuzhong Zhao, name: Bowen Cao, name: Jiayu Ding, name: Yilin Jia, name: Tengchao Lv, name: Yupan Huang, name: Shaohan Huang, name: Nan Yang, name: Li Dong, name: Lei Cui, name: Tao Ge, name: Xun Wang, name: Huitian Jiao, name: Sun Mao, name: FNU Kartik, name: Si-Qing Chen, name: Wai Lam, name: Furu Wei
Published: 2025-10-13 13:36:32

Recent advances in agentic workflows have enabled the automation of tasks
such as professional document generation. However, they primarily focus on
textual quality, neglecting visual structure and style, which are crucial for
readability and engagement. This gap arises mainly from the absence of suitable
reward models to guide agentic workflows toward producing documents with
stronger structural and stylistic quality. To address this, we propose
DocReward, a document reward model that evaluates documents based on their
structure and style. We construct a multi-domain dataset DocPair of 117K paired
documents, covering 32 domains and 267 document types, each including a high-
and low-professionalism document with identical content but different structure
and style. This enables the model to evaluate professionalism comprehensively,
and in a textual-quality-agnostic way. DocReward is trained using the
Bradley-Terry loss to score documents, penalizing predictions that contradict
the annotated ranking. To assess the performance of reward models, we create a
test dataset containing document bundles ranked by well-educated human
evaluators. Notably, DocReward outperforms GPT-4o and GPT-5 in accuracy by 30.6
and 19.4 percentage points, respectively, demonstrating its superiority over
baselines. In an extrinsic evaluation of document generation, DocReward
achieves a significantly higher win rate of 60.8%, compared to GPT-5's 37.7%
win rate, demonstrating its utility in guiding generation agents toward
producing human-preferred documents.
Making Mathematical Reasoning Adaptive
Authors: name: Zhejian Lai, name: Xiang Geng, name: Zhijun Wang, name: Yang Bai, name: Jiahuan Li, name: Rongxiang Weng, name: Jingang Wang, name: Xuezhi Cao, name: Xunliang Cai, name: Shujian Huang
Published: 2025-10-06 09:30:05

Mathematical reasoning is a primary indicator of large language models (LLMs)
intelligence. However, existing LLMs exhibit failures of robustness and
generalization. This paper attributes these deficiencies to spurious reasoning,
i.e., producing answers from superficial features. To address this challenge,
we propose the AdaR framework to enable adaptive reasoning, wherein models rely
on problem-solving logic to produce answers. AdaR synthesizes logically
equivalent queries by varying variable values, and trains models with RLVR on
these data to penalize spurious logic while encouraging adaptive logic. To
improve data quality, we extract the problem-solving logic from the original
query and generate the corresponding answer by code execution, then apply a
sanity check. Experimental results demonstrate that AdaR improves robustness
and generalization, achieving substantial improvement in mathematical reasoning
while maintaining high data efficiency. Analysis indicates that data synthesis
and RLVR function in a coordinated manner to enable adaptive reasoning in LLMs.
Subsequent analyses derive key design insights into the effect of critical
factors and the applicability to instruct LLMs. Our project is available at
https://github.com/LaiZhejian/AdaR
Pathology-CoT: Learning Visual Chain-of-Thought Agent from Expert Whole
  Slide Image Diagnosis Behavior
Authors: name: Sheng Wang, name: Ruiming Wu, name: Charles Herndon, name: Yihang Liu, name: Shunsuke Koga, name: Jeanne Shen, name: Zhi Huang
Published: 2025-10-06 08:44:04

Diagnosing a whole-slide image is an interactive, multi-stage process
involving changes in magnification and movement between fields. Although recent
pathology foundation models are strong, practical agentic systems that decide
what field to examine next, adjust magnification, and deliver explainable
diagnoses are still lacking. The blocker is data: scalable, clinically aligned
supervision of expert viewing behavior that is tacit and experience-based, not
written in textbooks or online, and therefore absent from large language model
training. We introduce the AI Session Recorder, which works with standard WSI
viewers to unobtrusively record routine navigation and convert the viewer logs
into standardized behavioral commands (inspect or peek at discrete
magnifications) and bounding boxes. A lightweight human-in-the-loop review
turns AI-drafted rationales into the Pathology-CoT dataset, a form of paired
"where to look" and "why it matters" supervision produced at roughly six times
lower labeling time. Using this behavioral data, we build Pathologist-o3, a
two-stage agent that first proposes regions of interest and then performs
behavior-guided reasoning. On gastrointestinal lymph-node metastasis detection,
it achieved 84.5% precision, 100.0% recall, and 75.4% accuracy, exceeding the
state-of-the-art OpenAI o3 model and generalizing across backbones. To our
knowledge, this constitutes one of the first behavior-grounded agentic systems
in pathology. Turning everyday viewer logs into scalable, expert-validated
supervision, our framework makes agentic pathology practical and establishes a
path to human-aligned, upgradeable clinical AI.
FinAuditing: A Financial Taxonomy-Structured Multi-Document Benchmark
  for Evaluating LLMs
Authors: name: Yan Wang, name: Keyi Wang, name: Shanshan Yang, name: Jaisal Patel, name: Jeff Zhao, name: Fengran Mo, name: Xueqing Peng, name: Lingfei Qian, name: Jimin Huang, name: Guojun Xiong, name: Xiao-Yang Liu, name: Jian-Yun Nie
Published: 2025-10-10 00:41:55

The complexity of the Generally Accepted Accounting Principles (GAAP) and the
hierarchical structure of eXtensible Business Reporting Language (XBRL) filings
make financial auditing increasingly difficult to automate and verify. While
large language models (LLMs) have demonstrated strong capabilities in
unstructured text understanding, their ability to reason over structured,
interdependent, and taxonomy-driven financial documents remains largely
unexplored. To fill this gap, we introduce FinAuditing, the first
taxonomy-aligned, structure-aware, multi-document benchmark for evaluating LLMs
on financial auditing tasks. Built from real US-GAAP-compliant XBRL filings,
FinAuditing defines three complementary subtasks, FinSM for semantic
consistency, FinRE for relational consistency, and FinMR for numerical
consistency, each targeting a distinct aspect of structured auditing reasoning.
We further propose a unified evaluation framework integrating retrieval,
classification, and reasoning metrics across these subtasks. Extensive
zero-shot experiments on 13 state-of-the-art LLMs reveal that current models
perform inconsistently across semantic, relational, and mathematical
dimensions, with accuracy drops of up to 60-90% when reasoning over
hierarchical multi-document structures. Our findings expose the systematic
limitations of modern LLMs in taxonomy-grounded financial reasoning and
establish FinAuditing as a foundation for developing trustworthy,
structure-aware, and regulation-aligned financial intelligence systems. The
benchmark dataset is available at Hugging Face.
AdaViewPlanner: Adapting Video Diffusion Models for Viewpoint Planning
  in 4D Scenes
Authors: name: Yu Li, name: Menghan Xia, name: Gongye Liu, name: Jianhong Bai, name: Xintao Wang, name: Conglang Zhang, name: Yuxuan Lin, name: Ruihang Chu, name: Pengfei Wan, name: Yujiu Yang
Published: 2025-10-12 15:55:44

Recent Text-to-Video (T2V) models have demonstrated powerful capability in
visual simulation of real-world geometry and physical laws, indicating its
potential as implicit world models. Inspired by this, we explore the
feasibility of leveraging the video generation prior for viewpoint planning
from given 4D scenes, since videos internally accompany dynamic scenes with
natural viewpoints. To this end, we propose a two-stage paradigm to adapt
pre-trained T2V models for viewpoint prediction, in a compatible manner. First,
we inject the 4D scene representation into the pre-trained T2V model via an
adaptive learning branch, where the 4D scene is viewpoint-agnostic and the
conditional generated video embeds the viewpoints visually. Then, we formulate
viewpoint extraction as a hybrid-condition guided camera extrinsic denoising
process. Specifically, a camera extrinsic diffusion branch is further
introduced onto the pre-trained T2V model, by taking the generated video and 4D
scene as input. Experimental results show the superiority of our proposed
method over existing competitors, and ablation studies validate the
effectiveness of our key technical designs. To some extent, this work proves
the potential of video generation models toward 4D interaction in real world.
Self-Improving LLM Agents at Test-Time
Authors: name: Emre Can Acikgoz, name: Cheng Qian, name: Heng Ji, name: Dilek Hakkani-Tür, name: Gokhan Tur
Published: 2025-10-09 06:37:35

One paradigm of language model (LM) fine-tuning relies on creating large
training datasets, under the assumption that high quantity and diversity will
enable models to generalize to novel tasks after post-training. In practice,
gathering large sets of data is inefficient, and training on them is
prohibitively expensive; worse, there is no guarantee that the resulting model
will handle complex scenarios or generalize better. Moreover, existing
techniques rarely assess whether a training sample provides novel information
or is redundant with the knowledge already acquired by the model, resulting in
unnecessary costs. In this work, we explore a new test-time self-improvement
method to create more effective and generalizable agentic LMs on-the-fly. The
proposed algorithm can be summarized in three steps: (i) first it identifies
the samples that model struggles with (self-awareness), (ii) then generates
similar examples from detected uncertain samples (self-data augmentation), and
(iii) uses these newly generated samples at test-time fine-tuning
(self-improvement). We study two variants of this approach: Test-Time
Self-Improvement (TT-SI), where the same model generates additional training
examples from its own uncertain cases and then learns from them, and contrast
this approach with Test-Time Distillation (TT-D), where a stronger model
generates similar examples for uncertain cases, enabling student to adapt using
distilled supervision. Empirical evaluations across different agent benchmarks
demonstrate that TT-SI improves the performance with +5.48% absolute accuracy
gain on average across all benchmarks and surpasses other standard learning
methods, yet using 68x less training samples. Our findings highlight the
promise of TT-SI, demonstrating the potential of self-improvement algorithms at
test-time as a new paradigm for building more capable agents toward
self-evolution.
PEAR: Phase Entropy Aware Reward for Efficient Reasoning
Authors: name: Chen Huang, name: Wei Lu, name: Wenxuan Zhang
Published: 2025-10-09 10:04:31

Large Reasoning Models (LRMs) have achieved impressive performance on complex
reasoning tasks by generating detailed chain-of-thought (CoT) explanations.
However, these responses are often excessively long, containing redundant
reasoning steps that inflate inference cost and reduce usability. Controlling
the length of generated reasoning without sacrificing accuracy remains an open
challenge. Through a systematic empirical analysis, we reveal a consistent
positive correlation between model entropy and response length at different
reasoning stages across diverse LRMs: the thinking phase exhibits higher
entropy, reflecting exploratory behavior of longer responses, while the final
answer phase shows lower entropy, indicating a more deterministic solution.
This observation suggests that entropy at different reasoning stages can serve
as a control knob for balancing conciseness and performance. Based on this
insight, this paper introduces Phase Entropy Aware Reward (PEAR), a reward
mechanism that incorporating phase-dependent entropy into the reward design.
Instead of treating all tokens uniformly, PEAR penalize excessive entropy
during the thinking phase and allowing moderate exploration at the final answer
phase, which encourages models to generate concise reasoning traces that retain
sufficient flexibility to solve the task correctly. This enables adaptive
control of response length without relying on explicit length targets or rigid
truncation rules. Extensive experiments across four benchmarks demonstrate that
PEAR consistently reduces response length while sustaining competitive accuracy
across model scales. In addition, PEAR demonstrates strong out-of-distribution
(OOD) robustness beyond the training distribution. Our code is available at:
https://github.com/iNLP-Lab/PEAR.