Orionnnn/hub-stats huggingface dataset:
Sculptor: Empowering LLMs with Cognitive Agency via Active Context
  Management
Authors: name: Mo Li, name: L. H. Xu, name: Qitai Tan, name: Ting Cao, name: Yunxin Liu
Published: 2025-08-06 17:32:58

Large Language Models (LLMs) suffer from significant performance degradation
when processing long contexts due to proactive interference, where irrelevant
information in earlier parts of the context disrupts reasoning and memory
recall. While most research focuses on external memory systems to augment LLMs'
capabilities, we propose a complementary approach: empowering LLMs with Active
Context Management (ACM) tools to actively sculpt their internal working
memory. We introduce Sculptor, a framework that equips LLMs with three
categories of tools: (1) context fragmentation, (2) summary, hide, and restore,
and (3) intelligent search. Our approach enables LLMs to proactively manage
their attention and working memory, analogous to how humans selectively focus
on relevant information while filtering out distractions. Experimental
evaluation on information-sparse benchmarks-PI-LLM (proactive interference) and
NeedleBench Multi-Needle Reasoning-demonstrates that Sculptor significantly
improves performance even without specific training, leveraging LLMs' inherent
tool calling generalization capabilities. By enabling Active Context
Management, Sculptor not only mitigates proactive interference but also
provides a cognitive foundation for more reliable reasoning across diverse
long-context tasks-highlighting that explicit context-control strategies,
rather than merely larger token windows, are key to robustness at scale.
Sotopia-RL: Reward Design for Social Intelligence
Authors: name: Haofei Yu, name: Zhengyang Qi, name: Yining Zhao, name: Kolby Nottingham, name: Keyang Xuan, name: Bodhisattwa Prasad Majumder, name: Hao Zhu, name: Paul Pu Liang, name: Jiaxuan You
Published: 2025-08-05 20:43:42

Social intelligence has become a critical capability for large language
models (LLMs), enabling them to engage effectively in real-world social tasks
such as accommodation, persuasion, collaboration, and negotiation.
Reinforcement learning (RL) is a natural fit for training socially intelligent
agents because it allows models to learn sophisticated strategies directly
through social interactions. However, social interactions have two key
characteristics that set barriers for RL training: (1) partial observability,
where utterances have indirect and delayed effects that complicate credit
assignment, and (2) multi-dimensionality, where behaviors such as
rapport-building or knowledge-seeking contribute indirectly to goal
achievement. These characteristics make Markov decision process (MDP)-based RL
with single-dimensional episode-level rewards inefficient and unstable. To
address these challenges, we propose Sotopia-RL, a novel framework that refines
coarse episode-level feedback into utterance-level, multi-dimensional rewards.
Utterance-level credit assignment mitigates partial observability by
attributing outcomes to individual utterances, while multi-dimensional rewards
capture the full richness of social interactions and reduce reward hacking.
Experiments in Sotopia, an open-ended social learning environment, demonstrate
that Sotopia-RL achieves state-of-the-art social goal completion scores (7.17
on Sotopia-hard and 8.31 on Sotopia-full), significantly outperforming existing
approaches. Ablation studies confirm the necessity of both utterance-level
credit assignment and multi-dimensional reward design for RL training. Our
implementation is publicly available at:
https://github.com/sotopia-lab/sotopia-rl.
Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens
Authors: name: Chengshuai Zhao, name: Zhen Tan, name: Pingchuan Ma, name: Dawei Li, name: Bohan Jiang, name: Yancheng Wang, name: Yingzhen Yang, name: Huan Liu
Published: 2025-08-02 04:37:28

Chain-of-Thought (CoT) prompting has been shown to improve Large Language
Model (LLM) performance on various tasks. With this approach, LLMs appear to
produce human-like reasoning steps before providing answers (a.k.a., CoT
reasoning), which often leads to the perception that they engage in deliberate
inferential processes. However, some initial findings suggest that CoT
reasoning may be more superficial than it appears, motivating us to explore
further. In this paper, we study CoT reasoning via a data distribution lens and
investigate if CoT reasoning reflects a structured inductive bias learned from
in-distribution data, allowing the model to conditionally generate reasoning
paths that approximate those seen during training. Thus, its effectiveness is
fundamentally bounded by the degree of distribution discrepancy between the
training data and the test queries. With this lens, we dissect CoT reasoning
via three dimensions: task, length, and format. To investigate each dimension,
we design DataAlchemy, an isolated and controlled environment to train LLMs
from scratch and systematically probe them under various distribution
conditions. Our results reveal that CoT reasoning is a brittle mirage that
vanishes when it is pushed beyond training distributions. This work offers a
deeper understanding of why and when CoT reasoning fails, emphasizing the
ongoing challenge of achieving genuine and generalizable reasoning.
Gaussian Variation Field Diffusion for High-fidelity Video-to-4D
  Synthesis
Authors: name: Bowen Zhang, name: Sicheng Xu, name: Chuxin Wang, name: Jiaolong Yang, name: Feng Zhao, name: Dong Chen, name: Baining Guo
Published: 2025-07-31 17:59:51

In this paper, we present a novel framework for video-to-4D generation that
creates high-quality dynamic 3D content from single video inputs. Direct 4D
diffusion modeling is extremely challenging due to costly data construction and
the high-dimensional nature of jointly representing 3D shape, appearance, and
motion. We address these challenges by introducing a Direct 4DMesh-to-GS
Variation Field VAE that directly encodes canonical Gaussian Splats (GS) and
their temporal variations from 3D animation data without per-instance fitting,
and compresses high-dimensional animations into a compact latent space.
Building upon this efficient representation, we train a Gaussian Variation
Field diffusion model with temporal-aware Diffusion Transformer conditioned on
input videos and canonical GS. Trained on carefully-curated animatable 3D
objects from the Objaverse dataset, our model demonstrates superior generation
quality compared to existing methods. It also exhibits remarkable
generalization to in-the-wild video inputs despite being trained exclusively on
synthetic data, paving the way for generating high-quality animated 3D content.
Project page: https://gvfdiffusion.github.io/.
A Coarse-to-Fine Approach to Multi-Modality 3D Occupancy Grounding
Authors: name: Zhan Shi, name: Song Wang, name: Junbo Chen, name: Jianke Zhu
Published: 2025-08-02 05:05:50

Visual grounding aims to identify objects or regions in a scene based on
natural language descriptions, essential for spatially aware perception in
autonomous driving. However, existing visual grounding tasks typically depend
on bounding boxes that often fail to capture fine-grained details. Not all
voxels within a bounding box are occupied, resulting in inaccurate object
representations. To address this, we introduce a benchmark for 3D occupancy
grounding in challenging outdoor scenes. Built on the nuScenes dataset, it
integrates natural language with voxel-level occupancy annotations, offering
more precise object perception compared to the traditional grounding task.
Moreover, we propose GroundingOcc, an end-to-end model designed for 3D
occupancy grounding through multi-modal learning. It combines visual, textual,
and point cloud features to predict object location and occupancy information
from coarse to fine. Specifically, GroundingOcc comprises a multimodal encoder
for feature extraction, an occupancy head for voxel-wise predictions, and a
grounding head to refine localization. Additionally, a 2D grounding module and
a depth estimation module enhance geometric understanding, thereby boosting
model performance. Extensive experiments on the benchmark demonstrate that our
method outperforms existing baselines on 3D occupancy grounding. The dataset is
available at https://github.com/RONINGOD/GroundingOcc.
HPSv3: Towards Wide-Spectrum Human Preference Score
Authors: name: Yuhang Ma, name: Xiaoshi Wu, name: Keqiang Sun, name: Hongsheng Li
Published: 2025-08-05 17:17:13

Evaluating text-to-image generation models requires alignment with human
perception, yet existing human-centric metrics are constrained by limited data
coverage, suboptimal feature extraction, and inefficient loss functions. To
address these challenges, we introduce Human Preference Score v3 (HPSv3). (1)
We release HPDv3, the first wide-spectrum human preference dataset integrating
1.08M text-image pairs and 1.17M annotated pairwise comparisons from
state-of-the-art generative models and low to high-quality real-world images.
(2) We introduce a VLM-based preference model trained using an
uncertainty-aware ranking loss for fine-grained ranking. Besides, we propose
Chain-of-Human-Preference (CoHP), an iterative image refinement method that
enhances quality without extra data, using HPSv3 to select the best image at
each step. Extensive experiments demonstrate that HPSv3 serves as a robust
metric for wide-spectrum image evaluation, and CoHP offers an efficient and
human-aligned approach to improve image generation quality. The code and
dataset are available at the HPSv3 Homepage.
LeanK: Learnable K Cache Channel Pruning for Efficient Decoding
Authors: name: Yike Zhang, name: Zhiyuan He, name: Huiqiang Jiang, name: Chengruidong Zhang, name: Yuqing Yang, name: Jianyong Wang, name: Lili Qiu
Published: 2025-08-04 09:08:43

Large language models (LLMs) enable long-context tasks but face efficiency
challenges due to the growing key-value (KV) cache. We propose LeanK, a
learning-based method that prunes unimportant key (K) cache channels by
leveraging static channel sparsity. With a novel two-stage training process,
LeanK learns channel-wise static mask that could satisfy specific sparsity
ratio and hardware alignment requirement. LeanK reduces GPU memory and
accelerates decoding without sacrificing accuracy. Experiments demonstrate up
to 70% K cache and 16%-18% V cache memory reduction. Custom decoding kernel
enables 1.3x speedup for attention computation. We also provide insights into
model channels and attention heads during long-context inference by analyzing
the learned importance distribution. Our code is available at
https://aka.ms/LeanK.
Position: The Current AI Conference Model is Unsustainable! Diagnosing
  the Crisis of Centralized AI Conference
Authors: name: Nuo Chen, name: Moming Duan, name: Andre Huikai Lin, name: Qian Wang, name: Jiaying Wu, name: Bingsheng He
Published: 2025-08-06 16:08:27

Artificial Intelligence (AI) conferences are essential for advancing
research, sharing knowledge, and fostering academic community. However, their
rapid expansion has rendered the centralized conference model increasingly
unsustainable. This paper offers a data-driven diagnosis of a structural crisis
that threatens the foundational goals of scientific dissemination, equity, and
community well-being. We identify four key areas of strain: (1) scientifically,
with per-author publication rates more than doubling over the past decade to
over 4.5 papers annually; (2) environmentally, with the carbon footprint of a
single conference exceeding the daily emissions of its host city; (3)
psychologically, with 71% of online community discourse reflecting negative
sentiment and 35% referencing mental health concerns; and (4) logistically,
with attendance at top conferences such as NeurIPS 2024 beginning to outpace
venue capacity. These pressures point to a system that is misaligned with its
core mission. In response, we propose the Community-Federated Conference (CFC)
model, which separates peer review, presentation, and networking into globally
coordinated but locally organized components, offering a more sustainable,
inclusive, and resilient path forward for AI research.
AttnTrace: Attention-based Context Traceback for Long-Context LLMs
Authors: name: Yanting Wang, name: Runpeng Geng, name: Ying Chen, name: Jinyuan Jia
Published: 2025-08-05 17:56:51

Long-context large language models (LLMs), such as Gemini-2.5-Pro and
Claude-Sonnet-4, are increasingly used to empower advanced AI systems,
including retrieval-augmented generation (RAG) pipelines and autonomous agents.
In these systems, an LLM receives an instruction along with a context--often
consisting of texts retrieved from a knowledge database or memory--and
generates a response that is contextually grounded by following the
instruction. Recent studies have designed solutions to trace back to a subset
of texts in the context that contributes most to the response generated by the
LLM. These solutions have numerous real-world applications, including
performing post-attack forensic analysis and improving the interpretability and
trustworthiness of LLM outputs. While significant efforts have been made,
state-of-the-art solutions such as TracLLM often lead to a high computation
cost, e.g., it takes TracLLM hundreds of seconds to perform traceback for a
single response-context pair. In this work, we propose AttnTrace, a new context
traceback method based on the attention weights produced by an LLM for a
prompt. To effectively utilize attention weights, we introduce two techniques
designed to enhance the effectiveness of AttnTrace, and we provide theoretical
insights for our design choice. We also perform a systematic evaluation for
AttnTrace. The results demonstrate that AttnTrace is more accurate and
efficient than existing state-of-the-art context traceback methods. We also
show that AttnTrace can improve state-of-the-art methods in detecting prompt
injection under long contexts through the attribution-before-detection
paradigm. As a real-world application, we demonstrate that AttnTrace can
effectively pinpoint injected instructions in a paper designed to manipulate
LLM-generated reviews. The code is at
https://github.com/Wang-Yanting/AttnTrace.
Bidirectional Likelihood Estimation with Multi-Modal Large Language
  Models for Text-Video Retrieval
Authors: name: Dohwan Ko, name: Ji Soo Lee, name: Minhyuk Choi, name: Zihang Meng, name: Hyunwoo J. Kim
Published: 2025-07-31 06:57:28

Text-Video Retrieval aims to find the most relevant text (or video) candidate
given a video (or text) query from large-scale online databases. Recent work
leverages multi-modal large language models (MLLMs) to improve retrieval,
especially for long or complex query-candidate pairs. However, we observe that
the naive application of MLLMs, i.e., retrieval based on candidate likelihood,
introduces candidate prior bias, favoring candidates with inherently higher
priors over those more relevant to the query. To this end, we propose a novel
retrieval framework, Bidirectional Likelihood Estimation with MLLM (BLiM),
which leverages both query and candidate likelihoods by training the model to
generate text from a given video as well as video features from a given text.
Furthermore, we introduce Candidate Prior Normalization (CPN), a simple yet
effective training-free score calibration module designed to mitigate candidate
prior bias in candidate likelihood. On four Text-Video Retrieval benchmarks,
our BLiM equipped with CPN outperforms previous state-of-the-art models by 6.4
R@1 on average, effectively alleviating candidate prior bias and emphasizing
query-candidate relevance. Our in-depth analysis across various multi-modal
tasks beyond retrieval highlights the broad applicability of CPN which enhances
visual understanding by reducing reliance on textual priors. Code is available
at https://github.com/mlvlab/BLiM.
What Is Your AI Agent Buying? Evaluation, Implications and Emerging
  Questions for Agentic E-Commerce
Authors: name: Amine Allouah, name: Omar Besbes, name: Josué D Figueroa, name: Yash Kanoria, name: Akshit Kumar
Published: 2025-08-04 17:19:36

Online marketplaces will be transformed by autonomous AI agents acting on
behalf of consumers. Rather than humans browsing and clicking,
vision-language-model (VLM) agents can parse webpages, evaluate products, and
transact. This raises a fundamental question: what do AI agents buy, and why?
We develop ACES, a sandbox environment that pairs a platform-agnostic VLM agent
with a fully programmable mock marketplace to study this question. We first
conduct basic rationality checks in the context of simple tasks, and then, by
randomizing product positions, prices, ratings, reviews, sponsored tags, and
platform endorsements, we obtain causal estimates of how frontier VLMs actually
shop. Models show strong but heterogeneous position effects: all favor the top
row, yet different models prefer different columns, undermining the assumption
of a universal "top" rank. They penalize sponsored tags and reward
endorsements. Sensitivities to price, ratings, and reviews are directionally
human-like but vary sharply in magnitude across models. Motivated by scenarios
where sellers use AI agents to optimize product listings, we show that a
seller-side agent that makes minor tweaks to product descriptions, targeting AI
buyer preferences, can deliver substantial market-share gains if AI-mediated
shopping dominates. We also find that modal product choices can differ across
models and, in some cases, demand may concentrate on a few select products,
raising competition questions. Together, our results illuminate how AI agents
may behave in e-commerce settings and surface concrete seller strategy,
platform design, and regulatory questions in an AI-mediated ecosystem.
TreeRanker: Fast and Model-agnostic Ranking System for Code Suggestions
  in IDEs
Authors: name: Daniele Cipollone, name: Egor Bogomolov, name: Arie van Deursen, name: Maliheh Izadi
Published: 2025-08-04 14:20:39

Token-level code completion is one of the most critical features in modern
Integrated Development Environments (IDEs). It assists developers by suggesting
relevant identifiers and APIs during coding. While completions are typically
derived from static analysis, their usefulness depends heavily on how they are
ranked, as correct predictions buried deep in the list are rarely seen by
users. Most current systems rely on hand-crafted heuristics or lightweight
machine learning models trained on user logs, which can be further improved to
capture context information and generalize across projects and coding styles.
In this work, we propose a new scoring approach to ranking static completions
using language models in a lightweight and model-agnostic way. Our method
organizes all valid completions into a prefix tree and performs a single greedy
decoding pass to collect token-level scores across the tree. This enables a
precise token-aware ranking without needing beam search, prompt engineering, or
model adaptations. The approach is fast, architecture-agnostic, and compatible
with already deployed models for code completion. These findings highlight a
practical and effective pathway for integrating language models into already
existing tools within IDEs, and ultimately providing smarter and more
responsive developer assistance.
The Promise of RL for Autoregressive Image Editing
Authors: name: Saba Ahmadi, name: Rabiul Awal, name: Ankur Sikarwar, name: Amirhossein Kazemnejad, name: Ge Ya Luo, name: Juan A. Rodriguez, name: Sai Rajeswar, name: Siva Reddy, name: Christopher Pal, name: Benno Krojer, name: Aishwarya Agrawal
Published: 2025-08-01 23:47:29

We explore three strategies to enhance performance on a wide range of image
editing tasks: supervised fine-tuning (SFT), reinforcement learning (RL), and
Chain-of-Thought (CoT) reasoning. In order to study all these components in one
consistent framework, we adopt an autoregressive multimodal model that
processes textual and visual tokens in a unified manner. We find RL combined
with a large multi-modal LLM verifier to be the most effective of these
strategies. As a result, we release EARL: Editing with Autoregression and RL, a
strong RL-based image editing model that performs competitively on a diverse
range of edits compared to strong baselines, despite using much less training
data. Thus, EARL pushes the frontier of autoregressive multimodal models on
image editing. We release our code, training data, and trained models at
https://github.com/mair-lab/EARL.
UniEgoMotion: A Unified Model for Egocentric Motion Reconstruction,
  Forecasting, and Generation
Authors: name: Chaitanya Patel, name: Hiroki Nakamura, name: Yuta Kyuragi, name: Kazuki Kozuka, name: Juan Carlos Niebles, name: Ehsan Adeli
Published: 2025-08-02 00:41:20

Egocentric human motion generation and forecasting with scene-context is
crucial for enhancing AR/VR experiences, improving human-robot interaction,
advancing assistive technologies, and enabling adaptive healthcare solutions by
accurately predicting and simulating movement from a first-person perspective.
However, existing methods primarily focus on third-person motion synthesis with
structured 3D scene contexts, limiting their effectiveness in real-world
egocentric settings where limited field of view, frequent occlusions, and
dynamic cameras hinder scene perception. To bridge this gap, we introduce
Egocentric Motion Generation and Egocentric Motion Forecasting, two novel tasks
that utilize first-person images for scene-aware motion synthesis without
relying on explicit 3D scene. We propose UniEgoMotion, a unified conditional
motion diffusion model with a novel head-centric motion representation tailored
for egocentric devices. UniEgoMotion's simple yet effective design supports
egocentric motion reconstruction, forecasting, and generation from first-person
visual inputs in a unified framework. Unlike previous works that overlook scene
semantics, our model effectively extracts image-based scene context to infer
plausible 3D motion. To facilitate training, we introduce EE4D-Motion, a
large-scale dataset derived from EgoExo4D, augmented with pseudo-ground-truth
3D motion annotations. UniEgoMotion achieves state-of-the-art performance in
egocentric motion reconstruction and is the first to generate motion from a
single egocentric image. Extensive evaluations demonstrate the effectiveness of
our unified framework, setting a new benchmark for egocentric motion modeling
and unlocking new possibilities for egocentric applications.
HyCodePolicy: Hybrid Language Controllers for Multimodal Monitoring and
  Decision in Embodied Agents
Authors: name: Yibin Liu, name: Zhixuan Liang, name: Zanxin Chen, name: Tianxing Chen, name: Mengkang Hu, name: Wanxi Dong, name: Congsheng Xu, name: Zhaoming Han, name: Yusen Qin, name: Yao Mu
Published: 2025-08-04 17:18:14

Recent advances in multimodal large language models (MLLMs) have enabled
richer perceptual grounding for code policy generation in embodied agents.
However, most existing systems lack effective mechanisms to adaptively monitor
policy execution and repair codes during task completion. In this work, we
introduce HyCodePolicy, a hybrid language-based control framework that
systematically integrates code synthesis, geometric grounding, perceptual
monitoring, and iterative repair into a closed-loop programming cycle for
embodied agents. Technically, given a natural language instruction, our system
first decomposes it into subgoals and generates an initial executable program
grounded in object-centric geometric primitives. The program is then executed
in simulation, while a vision-language model (VLM) observes selected
checkpoints to detect and localize execution failures and infer failure
reasons. By fusing structured execution traces capturing program-level events
with VLM-based perceptual feedback, HyCodePolicy infers failure causes and
repairs programs. This hybrid dual feedback mechanism enables self-correcting
program synthesis with minimal human supervision. Our results demonstrate that
HyCodePolicy significantly improves the robustness and sample efficiency of
robot manipulation policies, offering a scalable strategy for integrating
multimodal reasoning into autonomous decision-making pipelines.