Orionnnn/hub-stats huggingface dataset:
HalluGuard: Evidence-Grounded Small Reasoning Models to Mitigate
  Hallucinations in Retrieval-Augmented Generation
Authors: name: Loris Bergeron, name: Ioana Buhnila, name: Jérôme François, name: Radu State
Published: 2025-10-01 13:28:20

Large Language Models (LLMs) excel in many NLP tasks but remain prone to
hallucinations, limiting trust in real-world applications. We present
HalluGuard, a 4B-parameter Small Reasoning Model (SRM) for mitigating
hallucinations in Retrieval-Augmented Generation (RAG). HalluGuard classifies
document-claim pairs as grounded or hallucinated and produces evidence-grounded
justifications for transparency. Our approach combines (i) a domain-agnostic
synthetic dataset derived from FineWeb and refined through multi-stage curation
and data reformation, (ii) synthetic grounded and hallucinated claims, and
(iii) preference-based fine-tuning with Odds Ratio Preference Optimization to
distill large-model reasoning into a smaller backbone. On the RAGTruth subset
of the LLM-AggreFact benchmark, HalluGuard achieves 84.0% balanced accuracy
(BAcc), rivaling specialized models, MiniCheck (7B; 84.0%) and Granite Guardian
3.3 (8B; 82.2%) while using roughly half their parameters. Over the full
benchmark it reaches 75.7% BAcc, matching larger general-purpose LLMs such as
GPT-4o (75.9%). We will release HalluGuard and datasets under Apache 2.0 upon
acceptance.
Human3R: Everyone Everywhere All at Once
Authors: name: Yue Chen, name: Xingyu Chen, name: Yuxuan Xue, name: Anpei Chen, name: Yuliang Xiu, name: Gerard Pons-Moll
Published: 2025-10-07 17:59:52

We present Human3R, a unified, feed-forward framework for online 4D
human-scene reconstruction, in the world frame, from casually captured
monocular videos. Unlike previous approaches that rely on multi-stage
pipelines, iterative contact-aware refinement between humans and scenes, and
heavy dependencies, e.g., human detection, depth estimation, and SLAM
pre-processing, Human3R jointly recovers global multi-person SMPL-X bodies
("everyone"), dense 3D scene ("everywhere"), and camera trajectories in a
single forward pass ("all-at-once"). Our method builds upon the 4D online
reconstruction model CUT3R, and uses parameter-efficient visual prompt tuning,
to strive to preserve CUT3R's rich spatiotemporal priors, while enabling direct
readout of multiple SMPL-X bodies. Human3R is a unified model that eliminates
heavy dependencies and iterative refinement. After being trained on the
relatively small-scale synthetic dataset BEDLAM for just one day on one GPU, it
achieves superior performance with remarkable efficiency: it reconstructs
multiple humans in a one-shot manner, along with 3D scenes, in one stage, at
real-time speed (15 FPS) with a low memory footprint (8 GB). Extensive
experiments demonstrate that Human3R delivers state-of-the-art or competitive
performance across tasks, including global human motion estimation, local human
mesh recovery, video depth estimation, and camera pose estimation, with a
single unified model. We hope that Human3R will serve as a simple yet strong
baseline, be easily extended for downstream applications.Code available in
https://fanegg.github.io/Human3R
MixReasoning: Switching Modes to Think
Authors: name: Haiquan Lu, name: Gongfan Fang, name: Xinyin Ma, name: Qi Li, name: Xinchao Wang
Published: 2025-10-07 15:46:34

Reasoning models enhance performance by tackling problems in a step-by-step
manner, decomposing them into sub-problems and exploring long chains of thought
before producing an answer. However, applying extended reasoning to every step
introduces substantial redundancy, as sub-problems vary widely in difficulty
and complexity: a small number of pivotal steps are genuinely challenging and
decisive for the final answer, while many others only involve straightforward
revisions or simple computations. Therefore, a natural idea is to endow
reasoning models with the ability to adaptively respond to this variation,
rather than treating all steps with the same level of elaboration. To this end,
we propose MixReasoning, a framework that dynamically adjusts the depth of
reasoning within a single response. The resulting chain of thought then becomes
a mixture of detailed reasoning on difficult steps and concise inference on
simpler ones. Experiments on GSM8K, MATH-500, and AIME show that MixReasoning
shortens reasoning length and substantially improves efficiency without
compromising accuracy.
Fathom-DeepResearch: Unlocking Long Horizon Information Retrieval and
  Synthesis for SLMs
Authors: name: Shreyas Singh, name: Kunal Singh, name: Pradeep Moturi
Published: 2025-09-28 22:58:11

Tool-integrated reasoning has emerged as a key focus for enabling agentic
applications. Among these, DeepResearch Agents have gained significant
attention for their strong performance on complex, open-ended
information-seeking tasks. We introduce Fathom-DeepResearch, an agentic system
composed of two specialized models. The first is Fathom-Search-4B, a DeepSearch
model trained from Qwen3-4B and optimized for evidence-based investigation
through live web search and targeted webpage querying. Its training combines
three advances: (i) DUETQA, a 5K-sample dataset generated via multi-agent
self-play that enforces strict web-search dependence and heterogeneous source
grounding; (ii) RAPO, a zero-overhead extension of GRPO that stabilizes
multi-turn Reinforcement Learning with Verifiable Rewards through curriculum
pruning, reward-aware advantage scaling, and per-prompt replay buffers; and
(iii) a steerable step-level reward that classifies each tool call by cognitive
behavior and marginal utility, enabling explicit control over search trajectory
breadth, depth, and horizon. These improvements enable reliable extension of
tool-calling beyond 20 calls when warranted. The second is
Fathom-Synthesizer-4B, trained from Qwen3-4B, which converts multi-turn
DeepSearch traces into structured, citation-dense DeepResearch Reports for
comprehensive synthesis. Evaluated on DeepSearch benchmarks (SimpleQA, FRAMES,
WebWalker, Seal0, MuSiQue) and DeepResearch-Bench, the system achieves
state-of-the-art performance in the open-weights category while demonstrating
strong generalization to diverse reasoning tasks including HLE, AIME-25,
GPQA-Diamond, and MedQA.
Refusal Falls off a Cliff: How Safety Alignment Fails in Reasoning?
Authors: name: Qingyu Yin, name: Chak Tou Leong, name: Linyi Yang, name: Wenxuan Huang, name: Wenjie Li, name: Xiting Wang, name: Jaehong Yoon, name: YunXing, name: XingYu, name: Jinjin Gu
Published: 2025-10-07 15:32:59

Large reasoning models (LRMs) with multi-step reasoning capabilities have
shown remarkable problem-solving abilities, yet they exhibit concerning safety
vulnerabilities that remain poorly understood. In this work, we investigate why
safety alignment fails in reasoning models through a mechanistic
interpretability lens. Using a linear probing approach to trace refusal
intentions across token positions, we discover a striking phenomenon termed as
refusal cliff: many poorly-aligned reasoning models correctly identify
harmful prompts and maintain strong refusal intentions during their thinking
process, but experience a sharp drop in refusal scores at the final tokens
before output generation. This suggests that these models are not inherently
unsafe; rather, their refusal intentions are systematically suppressed. Through
causal intervention analysis, we identify a sparse set of attention heads that
negatively contribute to refusal behavior. Ablating just 3\% of these heads can
reduce attack success rates below 10\%. Building on these mechanistic insights,
we propose Cliff-as-a-Judge, a novel data selection method that
identifies training examples exhibiting the largest refusal cliff to
efficiently repair reasoning models' safety alignment. This approach achieves
comparable safety improvements using only 1.7\% of the vanilla safety training
data, demonstrating a less-is-more effect in safety alignment.
No Tokens Wasted: Leveraging Long Context in Biomedical Vision-Language
  Models
Authors: name: Min Woo Sun, name: Alejandro Lozano, name: Javier Gamazo Tejero, name: Vishwesh Nath, name: Xiao Xiao Sun, name: James Burgess, name: Yuhui Zhang, name: Kun Yuan, name: Robert Tibshirani, name: Sean Huver, name: Serena Yeung-Levy
Published: 2025-10-04 23:38:18

Embedding vision-language models (VLMs) are typically pretrained with short
text windows (<77 tokens), which forces the truncation of long-format captions.
Yet, the distribution of biomedical captions from large-scale open source
literature reveals that a huge portion of captions far exceed 77 tokens. To
this end, we investigate the impact of pretraining on long-format biomedical
captions by extending the context length of text encoders in VLMs. We find that
longer context (thus, enabling additional supervision provided in long-format
captions) correlates with better retrieval and classification performance.
Given this finding, we introduce BIOMEDICA-LongCAP, a dataset of 1M
image-caption pairs enriched with context-aware descriptions from full-text
articles, providing longer and additional textual supervision. Using
BIOMEDICA-LongCAP, we train BMC-LongCLIP, a long-context biomedical VLM with a
text encoder supporting windows of up to 512 tokens. Our model extends context
capacity by 6.6x, reducing token waste from 55% to just 2.2%. On long-caption
retrieval benchmarks, BMC-LongCLIP achieves up to +30% absolute gains in
Recall@1 and +2% average improvements in classification, while also converging
faster than short-context. Our results demonstrate that long-context modeling
is a promising direction for advancing biomedical VLMs.
ASPO: Asymmetric Importance Sampling Policy Optimization
Authors: name: Jiakang Wang, name: Runze Liu, name: Lei Lin, name: Wenping Hu, name: Xiu Li, name: Fuzheng Zhang, name: Guorui Zhou, name: Kun Gai
Published: 2025-10-07 15:54:24

Recent Large Language Model (LLM) post-training methods rely on token-level
clipping mechanisms during Reinforcement Learning (RL). However, we identify a
fundamental flaw in this Outcome-Supervised RL (OSRL) paradigm: the Importance
Sampling (IS) ratios of positive-advantage tokens are mismatched, leading to
unbalanced token weighting for positive and negative tokens. This mismatch
suppresses the update of low-probability tokens while over-amplifying already
high-probability ones. To address this, we propose Asymmetric Importance
Sampling Policy Optimization (ASPO), which uses a simple yet effective strategy
that flips the IS ratios of positive-advantage tokens, aligning their update
direction with the learning dynamics of negative ones. AIS further incorporates
a soft dual-clipping mechanism to stabilize extreme updates while maintaining
gradient flow. Comprehensive experiments on coding and mathematical reasoning
benchmarks demonstrate that ASPO significantly mitigates premature convergence,
improves training stability, and enhances final performance over strong
GRPO-based baselines. Our analysis provides new insights into the role of
token-level weighting in OSRL and highlights the critical importance of
correcting IS in LLM RL. The code and models of ASPO are available at
https://github.com/wizard-III/Archer2.0.
CARE: Cognitive-reasoning Augmented Reinforcement for Emotional Support
  Conversation
Authors: name: Jie Zhu, name: Yuanchen Zhou, name: Shuo Jiang, name: Junhui Li, name: Lifan Guo, name: Feng Chen, name: Chi Zhang, name: Fang Kong
Published: 2025-09-30 03:19:50

Emotional Support Conversation (ESC) plays a vital role in alleviating
psychological stress and providing emotional value through dialogue. While
recent studies have largely focused on data augmentation and synthetic corpus
construction, they often overlook the deeper cognitive reasoning processes that
underpin effective emotional support. To address this gap, we propose
CARE, a novel framework that strengthens reasoning in ESC without
relying on large-scale synthetic data. CARE leverages the original ESC training
set to guide models in generating logically coherent and supportive responses,
thereby explicitly enhancing cognitive reasoning. Building on this foundation,
we further employ reinforcement learning to refine and reinforce the reasoning
process. Experimental results demonstrate that CARE significantly improves both
the logical soundness and supportive quality of responses, advancing the
development of empathetic, cognitively robust, and human-like emotional support
systems.
Mixing Mechanisms: How Language Models Retrieve Bound Entities
  In-Context
Authors: name: Yoav Gur-Arieh, name: Mor Geva, name: Atticus Geiger
Published: 2025-10-07 17:44:30

A key component of in-context reasoning is the ability of language models
(LMs) to bind entities for later retrieval. For example, an LM might represent
"Ann loves pie" by binding "Ann" to "pie", allowing it to later retrieve "Ann"
when asked "Who loves pie?" Prior research on short lists of bound entities
found strong evidence that LMs implement such retrieval via a positional
mechanism, where "Ann" is retrieved based on its position in context. In this
work, we find that this mechanism generalizes poorly to more complex settings;
as the number of bound entities in context increases, the positional mechanism
becomes noisy and unreliable in middle positions. To compensate for this, we
find that LMs supplement the positional mechanism with a lexical mechanism
(retrieving "Ann" using its bound counterpart "pie") and a reflexive mechanism
(retrieving "Ann" through a direct pointer). Through extensive experiments on
nine models and ten binding tasks, we uncover a consistent pattern in how LMs
mix these mechanisms to drive model behavior. We leverage these insights to
develop a causal model combining all three mechanisms that estimates next token
distributions with 95% agreement. Finally, we show that our model generalizes
to substantially longer inputs of open-ended text interleaved with entity
groups, further demonstrating the robustness of our findings in more natural
settings. Overall, our study establishes a more complete picture of how LMs
bind and retrieve entities in-context.
VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation
Authors: name: Lesly Miculicich, name: Mihir Parmar, name: Hamid Palangi, name: Krishnamurthy Dj Dvijotham, name: Mirko Montanari, name: Tomas Pfister, name: Long T. Le
Published: 2025-10-03 04:11:43

The deployment of autonomous AI agents in sensitive domains, such as
healthcare, introduces critical risks to safety, security, and privacy. These
agents may deviate from user objectives, violate data handling policies, or be
compromised by adversarial attacks. Mitigating these dangers necessitates a
mechanism to formally guarantee that an agent's actions adhere to predefined
safety constraints, a challenge that existing systems do not fully address. We
introduce VeriGuard, a novel framework that provides formal safety guarantees
for LLM-based agents through a dual-stage architecture designed for robust and
verifiable correctness. The initial offline stage involves a comprehensive
validation process. It begins by clarifying user intent to establish precise
safety specifications. VeriGuard then synthesizes a behavioral policy and
subjects it to both testing and formal verification to prove its compliance
with these specifications. This iterative process refines the policy until it
is deemed correct. Subsequently, the second stage provides online action
monitoring, where VeriGuard operates as a runtime monitor to validate each
proposed agent action against the pre-verified policy before execution. This
separation of the exhaustive offline validation from the lightweight online
monitoring allows formal guarantees to be practically applied, providing a
robust safeguard that substantially improves the trustworthiness of LLM agents.
Discrete Diffusion Models with MLLMs for Unified Medical Multimodal
  Generation
Authors: name: Jiawei Mao, name: Yuhan Wang, name: Lifeng Chen, name: Can Zhao, name: Yucheng Tang, name: Dong Yang, name: Liangqiong Qu, name: Daguang Xu, name: Yuyin Zhou
Published: 2025-10-07 17:06:57

Recent advances in generative medical models are constrained by
modality-specific scenarios that hinder the integration of complementary
evidence from imaging, pathology, and clinical notes. This fragmentation limits
their evolution into foundation models that can learn and reason across the
full spectrum of biomedical data. We propose MeDiM, the first medical discrete
diffusion model that learns shared distributions across modalities without
modality-specific components. MeDiM unifies multiple generative tasks:
translating between images and text, and jointly producing image-report pairs
across domains in response to prompts. Built on a discrete diffusion framework,
MeDiM bridges vision and language representations through a shared
probabilistic space. To enable unified and flexible medical generation, we
employ a multimodal large language model (MLLM) as the diffusion backbone,
leveraging its prior knowledge and cross-modal reasoning. Two key designs are
introduced: (1) removing the causal attention mask for bidirectional context,
and (2) injecting continuous timestep embeddings for diffusion awareness.
Experiments demonstrate high-fidelity medical generation (FID 16.60 on
MIMIC-CXR and FID 24.19 on PathGen) and accurate report generation (METEOR
0.2650 and 0.2580). Jointly generated image-report pairs further enhance
downstream performance (plus6.43 percent BLEU-1, plus18.57 percent BLEU-2,
plus31.58 percent BLEU-3, plus4.80 percent METEOR), showing that MeDiM supports
coherent and clinically grounded multimodal outputs.
Scaling Code-Assisted Chain-of-Thoughts and Instructions for Model
  Reasoning
Authors: name: Honglin Lin, name: Qizhi Pei, name: Xin Gao, name: Zhuoshi Pan, name: Yu Li, name: Juntao Li, name: Conghui He, name: Lijun Wu
Published: 2025-10-05 07:59:24

Reasoning capability is pivotal for Large Language Models (LLMs) to solve
complex tasks, yet achieving reliable and scalable reasoning remains
challenging. While Chain-of-Thought (CoT) prompting has become a mainstream
approach, existing methods often suffer from uncontrolled generation,
insufficient quality, and limited diversity in reasoning paths. Recent efforts
leverage code to enhance CoT by grounding reasoning in executable steps, but
such methods are typically constrained to predefined mathematical problems,
hindering scalability and generalizability. In this work, we propose Caco
(Code-Assisted Chain-of-ThOught), a novel framework that automates the
synthesis of high-quality, verifiable, and diverse instruction-CoT reasoning
data through code-driven augmentation. Unlike prior work, Caco first fine-tunes
a code-based CoT generator on existing math and programming solutions in a
unified code format, then scales the data generation to a large amount of
diverse reasoning traces. Crucially, we introduce automated validation via code
execution and rule-based filtering to ensure logical correctness and structural
diversity, followed by reverse-engineering filtered outputs into natural
language instructions and language CoTs to enrich task adaptability. This
closed-loop process enables fully automated, scalable synthesis of reasoning
data with guaranteed executability. Experiments on our created Caco-1.3M
dataset demonstrate that Caco-trained models achieve strong competitive
performance on mathematical reasoning benchmarks, outperforming existing strong
baselines. Further analysis reveals that Caco's code-anchored verification and
instruction diversity contribute to superior generalization across unseen
tasks. Our work establishes a paradigm for building self-sustaining,
trustworthy reasoning systems without human intervention.
LightCache: Memory-Efficient, Training-Free Acceleration for Video
  Generation
Authors: name: Yang Xiao, name: Gen Li, name: Kaiyuan Deng, name: Yushu Wu, name: Zheng Zhan, name: Yanzhi Wang, name: Xiaolong Ma, name: Bo Hui
Published: 2025-10-06 20:54:44

Training-free acceleration has emerged as an advanced research area in video
generation based on diffusion models. The redundancy of latents in diffusion
model inference provides a natural entry point for acceleration. In this paper,
we decompose the inference process into the encoding, denoising, and decoding
stages, and observe that cache-based acceleration methods often lead to
substantial memory surges in the latter two stages. To address this problem, we
analyze the characteristics of inference across different stages and propose
stage-specific strategies for reducing memory consumption: 1) Asynchronous
Cache Swapping. 2) Feature chunk. 3) Slicing latents to decode. At the same
time, we ensure that the time overhead introduced by these three strategies
remains lower than the acceleration gains themselves. Compared with the
baseline, our approach achieves faster inference speed and lower memory usage,
while maintaining quality degradation within an acceptable range. The Code is
available at https://github.com/NKUShaw/LightCache .
HoloScene: Simulation-Ready Interactive 3D Worlds from a Single Video
Authors: name: Hongchi Xia, name: Chih-Hao Lin, name: Hao-Yu Hsu, name: Quentin Leboutet, name: Katelyn Gao, name: Michael Paulitsch, name: Benjamin Ummenhofer, name: Shenlong Wang
Published: 2025-10-07 04:12:18

Digitizing the physical world into accurate simulation-ready virtual
environments offers significant opportunities in a variety of fields such as
augmented and virtual reality, gaming, and robotics. However, current 3D
reconstruction and scene-understanding methods commonly fall short in one or
more critical aspects, such as geometry completeness, object interactivity,
physical plausibility, photorealistic rendering, or realistic physical
properties for reliable dynamic simulation. To address these limitations, we
introduce HoloScene, a novel interactive 3D reconstruction framework that
simultaneously achieves these requirements. HoloScene leverages a comprehensive
interactive scene-graph representation, encoding object geometry, appearance,
and physical properties alongside hierarchical and inter-object relationships.
Reconstruction is formulated as an energy-based optimization problem,
integrating observational data, physical constraints, and generative priors
into a unified, coherent objective. Optimization is efficiently performed via a
hybrid approach combining sampling-based exploration with gradient-based
refinement. The resulting digital twins exhibit complete and precise geometry,
physical stability, and realistic rendering from novel viewpoints. Evaluations
conducted on multiple benchmark datasets demonstrate superior performance,
while practical use-cases in interactive gaming and real-time digital-twin
manipulation illustrate HoloScene's broad applicability and effectiveness.
Project page: https://xiahongchi.github.io/HoloScene.