Orionnnn/hub-stats huggingface dataset:
Representation Shift: Unifying Token Compression with FlashAttention
Authors: name: Joonmyung Choi, name: Sanghyeok Lee, name: Byungoh Ko, name: Eunseo Kim, name: Jihyung Kil, name: Hyunwoo J. Kim
Published: 2025-08-01 06:53:55

Transformers have demonstrated remarkable success across vision, language,
and video. Yet, increasing task complexity has led to larger models and more
tokens, raising the quadratic cost of self-attention and the overhead of GPU
memory access. To reduce the computation cost of self-attention, prior work has
proposed token compression techniques that drop redundant or less informative
tokens. Meanwhile, fused attention kernels such as FlashAttention have been
developed to alleviate memory overhead by avoiding attention map construction
and its associated I/O to HBM. This, however, makes it incompatible with most
training-free token compression methods, which rely on attention maps to
determine token importance. Here, we propose Representation Shift, a
training-free, model-agnostic metric that measures the degree of change in each
token's representation. This seamlessly integrates token compression with
FlashAttention, without attention maps or retraining. Our method further
generalizes beyond Transformers to CNNs and state space models. Extensive
experiments show that Representation Shift enables effective token compression
compatible with FlashAttention, yielding significant speedups of up to 5.5% and
4.4% in video-text retrieval and video QA, respectively. Code is available at
https://github.com/mlvlab/Representation-Shift.
ChartCap: Mitigating Hallucination of Dense Chart Captioning
Authors: name: Junyoung Lim, name: Jaewoo Ahn, name: Gunhee Kim
Published: 2025-08-05 07:09:07

Generating accurate, informative, and hallucination-free captions for charts
remains challenging for vision language models, primarily due to the lack of
large-scale, high-quality datasets of real-world charts. However, existing
real-world chart datasets suffer from the inclusion of extraneous information
that cannot be inferred from the chart and failure to sufficiently capture
structural elements and key insights. Therefore, we introduce ChartCap, a
large-scale dataset of 565K real-world chart images paired with type-specific,
dense captions that exclude extraneous information and highlight both
structural elements and key insights in detail. To build ChartCap, we design a
four-stage pipeline that generates captions using only the discernible data
from the chart and employ a cycle consistency-based human verification, which
accelerates quality control without sacrificing accuracy. Additionally, we
propose a novel metric, the Visual Consistency Score, which evaluates caption
quality by measuring the similarity between the chart regenerated from a
caption and the original chart, independent of reference captions. Extensive
experiments confirms that models fine-tuned on ChartCap consistently generate
more accurate and informative captions with reduced hallucinations, surpassing
both open-source and proprietary models and even human-annotated captions.
Goedel-Prover-V2: Scaling Formal Theorem Proving with Scaffolded Data
  Synthesis and Self-Correction
Authors: name: Yong Lin, name: Shange Tang, name: Bohan Lyu, name: Ziran Yang, name: Jui-Hui Chung, name: Haoyu Zhao, name: Lai Jiang, name: Yihan Geng, name: Jiawei Ge, name: Jingruo Sun, name: Jiayun Wu, name: Jiri Gesi, name: Ximing Lu, name: David Acuna, name: Kaiyu Yang, name: Hongzhou Lin, name: Yejin Choi, name: Danqi Chen, name: Sanjeev Arora, name: Chi Jin
Published: 2025-08-05 16:28:22

We introduce Goedel-Prover-V2, a series of open-source language models that
set a new state-of-the-art in automated theorem proving. Built on the standard
expert iteration and reinforcement learning pipeline, our approach incorporates
three key innovations: (1) Scaffolded data synthesis: We generate synthetic
tasks of increasing difficulty to train the model to master increasingly
complex theorems; (2) Verifier-guided self-correction: We enable the model to
iteratively revise its proofs by leveraging feedback from the Lean compiler;
(3) Model averaging: We merge model checkpoints to mitigate the decrease in
model output diversity in later stages of training. Our small model,
Goedel-Prover-V2-8B, reaches 84.6% pass@32 on MiniF2F and outperforms
DeepSeek-Prover-V2-671B under the same metric, despite being 80X smaller. Our
flagship model, Goedel-Prover-V2-32B, achieves 88.1% on MiniF2F at pass@32 in
standard mode and 90.4% in self-correction mode, outperforming prior SOTA by a
large margin. Additionally, our flagship model solves 86 problems on
PutnamBench at pass@184, securing the first place among open-source models on
the leaderboard, surpassing DeepSeek-Prover-V2-671B's record of solving 47
problems by pass@1024 with a significantly smaller model size and compute
budget. At the time of its release (July-August 2025), Goedel-Prover-V2
achieves the strongest overall performance among all open-source theorem
provers. It also ranks among the top-performing models--including closed-source
systems with publicly reported performance--under a constrained test-time
compute budget. Our models, code, and data are released at
https://github.com/Goedel-LM/Goedel-Prover-V2.
LAMIC: Layout-Aware Multi-Image Composition via Scalability of
  Multimodal Diffusion Transformer
Authors: name: Yuzhuo Chen, name: Zehua Ma, name: Jianhua Wang, name: Kai Kang, name: Shunyu Yao, name: Weiming Zhang
Published: 2025-08-01 09:51:54

In controllable image synthesis, generating coherent and consistent images
from multiple references with spatial layout awareness remains an open
challenge. We present LAMIC, a Layout-Aware Multi-Image Composition framework
that, for the first time, extends single-reference diffusion models to
multi-reference scenarios in a training-free manner. Built upon the MMDiT
model, LAMIC introduces two plug-and-play attention mechanisms: 1) Group
Isolation Attention (GIA) to enhance entity disentanglement; and 2)
Region-Modulated Attention (RMA) to enable layout-aware generation. To
comprehensively evaluate model capabilities, we further introduce three
metrics: 1) Inclusion Ratio (IN-R) and Fill Ratio (FI-R) for assessing layout
control; and 2) Background Similarity (BG-S) for measuring background
consistency. Extensive experiments show that LAMIC achieves state-of-the-art
performance across most major metrics: it consistently outperforms existing
multi-reference baselines in ID-S, BG-S, IN-R and AVG scores across all
settings, and achieves the best DPG in complex composition tasks. These results
demonstrate LAMIC's superior abilities in identity keeping, background
preservation, layout control, and prompt-following, all achieved without any
training or fine-tuning, showcasing strong zero-shot generalization ability. By
inheriting the strengths of advanced single-reference models and enabling
seamless extension to multi-image scenarios, LAMIC establishes a new
training-free paradigm for controllable multi-image composition. As foundation
models continue to evolve, LAMIC's performance is expected to scale
accordingly. Our implementation is available at:
https://github.com/Suchenl/LAMIC.
CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and
  Outcome Reward
Authors: name: Shudong Liu, name: Hongwei Liu, name: Junnan Liu, name: Linchen Xiao, name: Songyang Gao, name: Chengqi Lyu, name: Yuzhe Gu, name: Wenwei Zhang, name: Derek F. Wong, name: Songyang Zhang, name: Kai Chen
Published: 2025-08-05 17:55:24

Answer verification is crucial not only for evaluating large language models
(LLMs) by matching their unstructured outputs against standard answers, but
also serves as the reward model to guide LLM optimization. Most evaluation
frameworks rely on regularized matching or employ general LLMs for answer
verification, which demands extensive, repetitive customization for regex rules
or evaluation prompts. Two fundamental limitations persist in current
methodologies: 1) the absence of comprehensive benchmarks that systematically
evaluate verification capabilities across different LLMs; and 2) the nascent
stage of verifier development, where existing approaches lack both the
robustness to handle complex edge cases and the generalizability across
different domains. In this work, we develop CompassVerifier, an accurate and
robust lightweight verifier model for evaluation and outcome reward. It
demonstrates multi-domain competency spanning math, knowledge, and diverse
reasoning tasks, with the capability to process various answer types, including
multi-subproblems, formulas, and sequence answers, while effectively
identifying abnormal/invalid responses. We introduce VerifierBench benchmark
comprising model outputs collected from multiple data sources, augmented
through manual analysis of metaerror patterns to enhance CompassVerifier. We
anticipate that CompassVerifier and VerifierBench will facilitate answer
verification, evaluation protocols, and reinforcement learning research. Code
and dataset are available at https://github.com/open-compass/CompassVerifier.
CRINN: Contrastive Reinforcement Learning for Approximate Nearest
  Neighbor Search
Authors: name: Xiaoya Li, name: Xiaofei Sun, name: Albert Wang, name: Chris Shum, name: Jiwei Li
Published: 2025-08-04 05:57:46

Approximate nearest-neighbor search (ANNS) algorithms have become
increasingly critical for recent AI applications, particularly in
retrieval-augmented generation (RAG) and agent-based LLM applications. In this
paper, we present CRINN, a new paradigm for ANNS algorithms. CRINN treats ANNS
optimization as a reinforcement learning problem where execution speed serves
as the reward signal. This approach enables the automatic generation of
progressively faster ANNS implementations while maintaining accuracy
constraints. Our experimental evaluation demonstrates CRINN's effectiveness
across six widely-used NNS benchmark datasets. When compared against
state-of-the-art open-source ANNS algorithms, CRINN achieves best performance
on three of them (GIST-960-Euclidean, MNIST-784-Euclidean, and
GloVe-25-angular), and tied for first place on two of them (SIFT-128-Euclidean
and GloVe-25-angular). The implications of CRINN's success reach well beyond
ANNS optimization: It validates that LLMs augmented with reinforcement learning
can function as an effective tool for automating sophisticated algorithmic
optimizations that demand specialized knowledge and labor-intensive manual
refinement.Code can be found at https://github.com/deepreinforce-ai/CRINN
Multi-human Interactive Talking Dataset
Authors: name: Zeyu Zhu, name: Weijia Wu, name: Mike Zheng Shou
Published: 2025-08-05 03:54:18

Existing studies on talking video generation have predominantly focused on
single-person monologues or isolated facial animations, limiting their
applicability to realistic multi-human interactions. To bridge this gap, we
introduce MIT, a large-scale dataset specifically designed for multi-human
talking video generation. To this end, we develop an automatic pipeline that
collects and annotates multi-person conversational videos. The resulting
dataset comprises 12 hours of high-resolution footage, each featuring two to
four speakers, with fine-grained annotations of body poses and speech
interactions. It captures natural conversational dynamics in multi-speaker
scenario, offering a rich resource for studying interactive visual behaviors.
To demonstrate the potential of MIT, we furthur propose CovOG, a baseline model
for this novel task. It integrates a Multi-Human Pose Encoder (MPE) to handle
varying numbers of speakers by aggregating individual pose embeddings, and an
Interactive Audio Driver (IAD) to modulate head dynamics based on
speaker-specific audio features. Together, these components showcase the
feasibility and challenges of generating realistic multi-human talking videos,
establishing MIT as a valuable benchmark for future research. The code is
avalibale at: https://github.com/showlab/Multi-human-Talking-Video-Dataset.
Seed Diffusion: A Large-Scale Diffusion Language Model with High-Speed
  Inference
Authors: name: Yuxuan Song, name: Zheng Zhang, name: Cheng Luo, name: Pengyang Gao, name: Fan Xia, name: Hao Luo, name: Zheng Li, name: Yuehang Yang, name: Hongli Yu, name: Xingwei Qu, name: Yuwei Fu, name: Jing Su, name: Ge Zhang, name: Wenhao Huang, name: Mingxuan Wang, name: Lin Yan, name: Xiaoying Jia, name: Jingjing Liu, name: Wei-Ying Ma, name: Ya-Qin Zhang, name: Yonghui Wu, name: Hao Zhou
Published: 2025-08-04 08:43:01

We present Seed Diffusion Preview, a large-scale language model based on
discrete-state diffusion, offering remarkably fast inference speed. Thanks to
non-sequential, parallel generation, discrete diffusion models provide a
notable speedup to mitigate the inherent latency of token-by-token decoding, as
demonstrated recently (e.g., Mercury Coder, Gemini Diffusion). Seed Diffusion
Preview achieves an inference speed of 2,146 token/s over H20 GPUs while
maintaining competitive performance across a sweep of standard code evaluation
benchmarks, significantly faster than contemporary Mercury and Gemini
Diffusion, establishing new state of the art on the speed-quality Pareto
frontier for code models.
Skywork UniPic: Unified Autoregressive Modeling for Visual Understanding
  and Generation
Authors: name: Peiyu Wang, name: Yi Peng, name: Yimeng Gan, name: Liang Hu, name: Tianyidan Xie, name: Xiaokun Wang, name: Yichen Wei, name: Chuanxin Tang, name: Bo Zhu, name: Changshi Li, name: Hongyang Wei, name: Eric Li, name: Xuchen Song, name: Yang Liu, name: Yahui Zhou
Published: 2025-08-05 10:59:01

We introduce Skywork UniPic, a 1.5 billion-parameter autoregressive model
that unifies image understanding, text-to-image generation, and image editing
within a single architecture-eliminating the need for task-specific adapters or
inter-module connectors-and demonstrate that compact multimodal systems can
achieve state-of-the-art performance on commodity hardware. Skywork UniPic
achieves a GenEval score of 0.86, surpassing most existing unified models; sets
a new DPG-Bench complex-generation record of 85.5; attains 5.83 on
GEditBench-EN and 3.49 on ImgEdit-Bench for image editing; and generates 1024 x
1024 images with under 15 GB of GPU memory (e.g., RTX 4090). (1) a decoupled
encoding strategy that leverages a masked autoregressive encoder for synthesis
and a SigLIP2 encoder for understanding, all feeding a shared autoregressive
decoder; (2) a progressive, resolution-aware training schedule scaling from 256
x 256 to 1024 x 1024 while dynamically unfreezing parameters to balance
capacity and stability; and (3) meticulously curated, 100 million-scale
datasets augmented with task-specific reward models to refine generation and
editing objectives. By demonstrating that high-fidelity multimodal integration
need not incur prohibitive resource demands, Skywork UniPic establishes a
practical paradigm for deployable, high-fidelity multimodal AI. Code and
weights are publicly available at
https://huggingface.co/Skywork/Skywork-UniPic-1.5B.
LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?
Authors: name: Guozhao Mo, name: Wenliang Zhong, name: Jiawei Chen, name: Xuanang Chen, name: Yaojie Lu, name: Hongyu Lin, name: Ben He, name: Xianpei Han, name: Le Sun
Published: 2025-08-03 14:36:42

With the rapid development of Model Context Protocol (MCP), the number of MCP
servers has surpassed 10,000. However, existing MCP benchmarks are limited to
single-server settings with only a few tools, hindering effective evaluation of
agent capabilities in large-scale, real-world scenarios. To address this
limitation, we present LiveMCPBench, the first comprehensive benchmark
comprising 95 real-world tasks grounded in the MCP ecosystem, designed to
evaluate LLM agents at scale across diverse servers. To support a scalable and
reproducible evaluation pipeline in large-scale MCP environments, we curate
LiveMCPTool, a diverse and readily deployable collection of 70 MCP servers and
527 tools. Furthermore, we introduce LiveMCPEval, an LLM-as-a-Judge framework
that enables automated and adaptive evaluation in dynamic, time-varying task
environments, achieving 81% agreement with human reviewers. Finally, we propose
the MCP Copilot Agent, a multi-step agent that routes tools for dynamic
planning and executes tools for API interaction across the entire LiveMCPTool
suite. Our evaluation covers 10 leading models, with the best-performing model
(Claude-Sonnet-4) reaching a 78.95% success rate. However, we observe large
performance variance across models, and several widely-used models perform
poorly in LiveMCPBench's complex, tool-rich environments. Overall, LiveMCPBench
offers the first unified framework for benchmarking LLM agents in realistic,
tool-rich, and dynamic MCP environments, laying a solid foundation for scalable
and reproducible research on agent capabilities. Our code and data will be
publicly available at https://icip-cas.github.io/LiveMCPBench.
Tool-integrated Reinforcement Learning for Repo Deep Search
Authors: name: Zexiong Ma, name: Chao Peng, name: Qunhong Zeng, name: Pengfei Gao, name: Yanzhen Zou, name: Bing Xie
Published: 2025-08-05 02:44:21

Issue localization, the process of identifying code locations that need
modification to resolve software issues, is a critical yet challenging task in
software development. The semantic gap between natural language issue
descriptions and faulty code requires complex multi-hop reasoning through code
dependencies. Existing LLM-based agents attempt to address this by integrating
repository retrieval tools. However, this transforms issue localization into a
demanding task we call Repo Deep Search, which requires the LLM to effectively
utilize various repository retrieval tools throughout a multi-step reasoning
and navigation process. To tackle this challenge, we present ToolTrain, a
two-stage tool-integrated training framework combining rejection-sampled
supervised fine-tuning and tool-integrated reinforcement learning to enhance
LLMs' ability to use retrieval tools for issue localization. Experimental
results show that ToolTrain-trained models achieve state-of-the-art
performance, with our 32B model even surpassing Claude-3.7 on function-level
localization. The results also show that improved localization performance
translates to better end-to-end issue resolution performance. This further
demonstrates that training for issue localization is a viable and effective
strategy for improving automated software development.
TRACEALIGN -- Tracing the Drift: Attributing Alignment Failures to
  Training-Time Belief Sources in LLMs
Authors: name: Amitava Das, name: Vinija Jain, name: Aman Chadha
Published: 2025-08-04 05:03:35

Large Language Models (LLMs) fine-tuned to align with human values often
exhibit alignment drift, producing unsafe or policy-violating completions when
exposed to adversarial prompts, decoding perturbations, or paraphrased
jailbreaks. While prior work has behaviorally characterized alignment failure,
little is known about the training-time belief sources underlying these
failures. We introduce TraceAlign, a unified framework for tracing unsafe
completions back to their root causes in the model's training corpus. Central
to our approach is the Belief Conflict Index (BCI), which quantifies semantic
inconsistency between generated spans and aligned policies, based on retrieved
training documents using suffix-array matching. We propose three complementary
interventions: (i) TraceShield, an inference-time safety filter that refuses
completions with high-BCI spans, (ii) Contrastive Belief Deconfliction Loss, a
contrastive fine-tuning objective penalizing high-BCI continuations during DPO,
and (iii) Prov-Decode, a provenance-aware decoding strategy that vetoes beam
expansions predicted to yield high-BCI spans. Together, these defenses reduce
alignment drift by up to 85% on our curated Alignment Drift Benchmark (ADB)
while preserving utility on standard tasks, with delta less than 0.2 and
improved refusal quality. We further derive a theoretical upper bound on drift
likelihood via suffix-array span statistics, linking memorization frequency and
length to adversarial reactivation risk. TraceAlign thus provides the first
scalable, traceable, and grounded toolkit for understanding and mitigating
alignment failures at source. To encourage further exploration and development,
we open-source our implementation at:
https://anonymous.4open.science/r/tracealign-2DA7
AlignGuard-LoRA: Alignment-Preserving Fine-Tuning via Fisher-Guided
  Decomposition and Riemannian-Geodesic Collision Regularization
Authors: name: Amitava Das, name: Abhilekh Borah, name: Vinija Jain, name: Aman Chadha
Published: 2025-08-04 05:45:24

Low-rank adaptation (LoRA) has become a standard tool for efficiently
fine-tuning large language models (LLMs). Yet, even minor LoRA updates can
induce alignment drift, weakening safety and behavioral constraints through
entangled parameter changes. To address this, we propose AlignGuard-LoRA (AGL),
a principled framework for preserving alignment during finetuning. AGL
introduces several key components: a primary task loss for supervision, Fisher
Information Matrix-based regularization to restrict updates in
alignment-sensitive subspaces, and task-specific regularization to stabilize
the integration of new knowledge. We further introduce collision-aware
regularization, blending Riemannian overlap -- which penalizes coordinate-wise
interference -- and geodesic separation -- which encourages disjoint update
geometry. We curate DriftCaps, a targeted diagnostic benchmark of safe and
unsafe prompts designed to quantify alignment drift and safety degradation.
Empirical evaluations show that AGL mitigates alignment drift by up to 50% on
safety-critical benchmarks without degrading downstream task performance.
Comprehensive ablation confirms that each component contributes distinctly to
preserving latent safety behaviors. Finally, we derive and validate a scaling
law for catastrophic forgetting, revealing that AGL flattens post-finetuning
loss escalation while preserving adaptation dynamics. AGL is a structurally
grounded refinement of LoRA, ensuring alignment preservation with minimal
trade-offs. To encourage further exploration and development, we open-source
our implementation.
LongVie: Multimodal-Guided Controllable Ultra-Long Video Generation
Authors: name: Jianxiong Gao, name: Zhaoxi Chen, name: Xian Liu, name: Jianfeng Feng, name: Chenyang Si, name: Yanwei Fu, name: Yu Qiao, name: Ziwei Liu
Published: 2025-08-05 17:59:58

Controllable ultra-long video generation is a fundamental yet challenging
task. Although existing methods are effective for short clips, they struggle to
scale due to issues such as temporal inconsistency and visual degradation. In
this paper, we initially investigate and identify three key factors: separate
noise initialization, independent control signal normalization, and the
limitations of single-modality guidance. To address these issues, we propose
LongVie, an end-to-end autoregressive framework for controllable long video
generation. LongVie introduces two core designs to ensure temporal consistency:
1) a unified noise initialization strategy that maintains consistent generation
across clips, and 2) global control signal normalization that enforces
alignment in the control space throughout the entire video. To mitigate visual
degradation, LongVie employs 3) a multi-modal control framework that integrates
both dense (e.g., depth maps) and sparse (e.g., keypoints) control signals,
complemented by 4) a degradation-aware training strategy that adaptively
balances modality contributions over time to preserve visual quality. We also
introduce LongVGenBench, a comprehensive benchmark consisting of 100
high-resolution videos spanning diverse real-world and synthetic environments,
each lasting over one minute. Extensive experiments show that LongVie achieves
state-of-the-art performance in long-range controllability, consistency, and
quality.