Orionnnn/hub-stats huggingface dataset:
Set You Straight: Auto-Steering Denoising Trajectories to Sidestep
  Unwanted Concepts
Authors: name: Leyang Li, name: Shilin Lu, name: Yan Ren, name: Adams Wai-Kin Kong
Published: 2025-04-17 09:29:30

Ensuring the ethical deployment of text-to-image models requires effective
techniques to prevent the generation of harmful or inappropriate content. While
concept erasure methods offer a promising solution, existing finetuning-based
approaches suffer from notable limitations. Anchor-free methods risk disrupting
sampling trajectories, leading to visual artifacts, while anchor-based methods
rely on the heuristic selection of anchor concepts. To overcome these
shortcomings, we introduce a finetuning framework, dubbed ANT, which
Automatically guides deNoising Trajectories to avoid unwanted concepts. ANT is
built on a key insight: reversing the condition direction of classifier-free
guidance during mid-to-late denoising stages enables precise content
modification without sacrificing early-stage structural integrity. This
inspires a trajectory-aware objective that preserves the integrity of the
early-stage score function field, which steers samples toward the natural image
manifold, without relying on heuristic anchor concept selection. For
single-concept erasure, we propose an augmentation-enhanced weight saliency map
to precisely identify the critical parameters that most significantly
contribute to the unwanted concept, enabling more thorough and efficient
erasure. For multi-concept erasure, our objective function offers a versatile
plug-and-play solution that significantly boosts performance. Extensive
experiments demonstrate that ANT achieves state-of-the-art results in both
single and multi-concept erasure, delivering high-quality, safe outputs without
compromising the generative fidelity. Code is available at
https://github.com/lileyang1210/ANT
Retrieval-Augmented Generation with Conflicting Evidence
Authors: name: Han Wang, name: Archiki Prasad, name: Elias Stengel-Eskin, name: Mohit Bansal
Published: 2025-04-17 16:46:11

Large language model (LLM) agents are increasingly employing
retrieval-augmented generation (RAG) to improve the factuality of their
responses. However, in practice, these systems often need to handle ambiguous
user queries and potentially conflicting information from multiple sources
while also suppressing inaccurate information from noisy or irrelevant
documents. Prior work has generally studied and addressed these challenges in
isolation, considering only one aspect at a time, such as handling ambiguity or
robustness to noise and misinformation. We instead consider multiple factors
simultaneously, proposing (i) RAMDocs (Retrieval with Ambiguity and
Misinformation in Documents), a new dataset that simulates complex and
realistic scenarios for conflicting evidence for a user query, including
ambiguity, misinformation, and noise; and (ii) MADAM-RAG, a multi-agent
approach in which LLM agents debate over the merits of an answer over multiple
rounds, allowing an aggregator to collate responses corresponding to
disambiguated entities while discarding misinformation and noise, thereby
handling diverse sources of conflict jointly. We demonstrate the effectiveness
of MADAM-RAG using both closed and open-source models on AmbigDocs -- which
requires presenting all valid answers for ambiguous queries -- improving over
strong RAG baselines by up to 11.40% and on FaithEval -- which requires
suppressing misinformation -- where we improve by up to 15.80% (absolute) with
Llama3.3-70B-Instruct. Furthermore, we find that RAMDocs poses a challenge for
existing RAG baselines (Llama3.3-70B-Instruct only obtains 32.60 exact match
score). While MADAM-RAG begins to address these conflicting factors, our
analysis indicates that a substantial gap remains especially when increasing
the level of imbalance in supporting evidence and misinformation.
Generate, but Verify: Reducing Hallucination in Vision-Language Models
  with Retrospective Resampling
Authors: name: Tsung-Han Wu, name: Heekyung Lee, name: Jiaxin Ge, name: Joseph E. Gonzalez, name: Trevor Darrell, name: David M. Chan
Published: 2025-04-17 17:59:22

Vision-Language Models (VLMs) excel at visual understanding but often suffer
from visual hallucinations, where they generate descriptions of nonexistent
objects, actions, or concepts, posing significant risks in safety-critical
applications. Existing hallucination mitigation methods typically follow one of
two paradigms: generation adjustment, which modifies decoding behavior to align
text with visual inputs, and post-hoc verification, where external models
assess and correct outputs. While effective, generation adjustment methods
often rely on heuristics and lack correction mechanisms, while post-hoc
verification is complicated, typically requiring multiple models and tending to
reject outputs rather than refine them. In this work, we introduce REVERSE, a
unified framework that integrates hallucination-aware training with on-the-fly
self-verification. By leveraging a new hallucination-verification dataset
containing over 1.3M semi-synthetic samples, along with a novel inference-time
retrospective resampling technique, our approach enables VLMs to both detect
hallucinations during generation and dynamically revise those hallucinations.
Our evaluations show that REVERSE achieves state-of-the-art hallucination
reduction, outperforming the best existing methods by up to 12% on CHAIR-MSCOCO
and 28% on HaloQuest. Our dataset, model, and code are available at:
https://reverse-vlm.github.io.
Antidistillation Sampling
Authors: name: Yash Savani, name: Asher Trockman, name: Zhili Feng, name: Avi Schwarzschild, name: Alexander Robey, name: Marc Finzi, name: J. Zico Kolter
Published: 2025-04-17 17:54:14

Frontier models that generate extended reasoning traces inadvertently produce
rich token sequences that can facilitate model distillation. Recognizing this
vulnerability, model owners may seek sampling strategies that limit the
effectiveness of distillation without compromising model performance.
Antidistillation sampling provides exactly this capability. By
strategically modifying a model's next-token probability distribution,
antidistillation sampling poisons reasoning traces, rendering them
significantly less effective for distillation while preserving the model's
practical utility. For further details, see https://antidistillation.com.
Packing Input Frame Context in Next-Frame Prediction Models for Video
  Generation
Authors: name: Lvmin Zhang, name: Maneesh Agrawala
Published: 2025-04-17 04:02:31

We present a neural network structure, FramePack, to train next-frame (or
next-frame-section) prediction models for video generation. The FramePack
compresses input frames to make the transformer context length a fixed number
regardless of the video length. As a result, we are able to process a large
number of frames using video diffusion with computation bottleneck similar to
image diffusion. This also makes the training video batch sizes significantly
higher (batch sizes become comparable to image diffusion training). We also
propose an anti-drifting sampling method that generates frames in inverted
temporal order with early-established endpoints to avoid exposure bias (error
accumulation over iterations). Finally, we show that existing video diffusion
models can be finetuned with FramePack, and their visual quality may be
improved because the next-frame prediction supports more balanced diffusion
schedulers with less extreme flow shift timesteps.
VistaDPO: Video Hierarchical Spatial-Temporal Direct Preference
  Optimization for Large Video Models
Authors: name: Haojian Huang, name: Haodong Chen, name: Shengqiong Wu, name: Meng Luo, name: Jinlan Fu, name: Xinya Du, name: Hanwang Zhang, name: Hao Fei
Published: 2025-04-17 17:39:41

Large Video Models (LVMs) built upon Large Language Models (LLMs) have shown
promise in video understanding but often suffer from misalignment with human
intuition and video hallucination issues. To address these challenges, we
introduce VistaDPO, a novel framework for Video Hierarchical Spatial-Temporal
Direct Preference Optimization. VistaDPO enhances text-video preference
alignment across three hierarchical levels: i) Instance Level, aligning overall
video content with responses; ii) Temporal Level, aligning video temporal
semantics with event descriptions; and iii) Perceptive Level, aligning spatial
objects with language tokens. Given the lack of datasets for fine-grained
video-language preference alignment, we construct VistaDPO-7k, a dataset of
7.2K QA pairs annotated with chosen and rejected responses, along with
spatial-temporal grounding information such as timestamps, keyframes, and
bounding boxes. Extensive experiments on benchmarks such as Video
Hallucination, Video QA, and Captioning performance tasks demonstrate that
VistaDPO significantly improves the performance of existing LVMs, effectively
mitigating video-language misalignment and hallucination. The code and data are
available at https://github.com/HaroldChen19/VistaDPO.
WORLDMEM: Long-term Consistent World Simulation with Memory
Authors: name: Zeqi Xiao, name: Yushi Lan, name: Yifan Zhou, name: Wenqi Ouyang, name: Shuai Yang, name: Yanhong Zeng, name: Xingang Pan
Published: 2025-04-16 17:59:30

World simulation has gained increasing popularity due to its ability to model
virtual environments and predict the consequences of actions. However, the
limited temporal context window often leads to failures in maintaining
long-term consistency, particularly in preserving 3D spatial consistency. In
this work, we present WorldMem, a framework that enhances scene generation with
a memory bank consisting of memory units that store memory frames and states
(e.g., poses and timestamps). By employing a memory attention mechanism that
effectively extracts relevant information from these memory frames based on
their states, our method is capable of accurately reconstructing previously
observed scenes, even under significant viewpoint or temporal gaps.
Furthermore, by incorporating timestamps into the states, our framework not
only models a static world but also captures its dynamic evolution over time,
enabling both perception and interaction within the simulated world. Extensive
experiments in both virtual and real scenarios validate the effectiveness of
our approach.
ChartQAPro: A More Diverse and Challenging Benchmark for Chart Question
  Answering
Authors: name: Ahmed Masry, name: Mohammed Saidul Islam, name: Mahir Ahmed, name: Aayush Bajaj, name: Firoz Kabir, name: Aaryaman Kartha, name: Md Tahmid Rahman Laskar, name: Mizanur Rahman, name: Shadikur Rahman, name: Mehrad Shahmohammadi, name: Megh Thakkar, name: Md Rizwan Parvez, name: Enamul Hoque, name: Shafiq Joty
Published: 2025-04-07 21:05:06

Charts are ubiquitous, as people often use them to analyze data, answer
questions, and discover critical insights. However, performing complex
analytical tasks with charts requires significant perceptual and cognitive
effort. Chart Question Answering (CQA) systems automate this process by
enabling models to interpret and reason with visual representations of data.
However, existing benchmarks like ChartQA lack real-world diversity and have
recently shown performance saturation with modern large vision-language models
(LVLMs). To address these limitations, we introduce ChartQAPro, a new benchmark
that includes 1,341 charts from 157 diverse sources, spanning various chart
types, including infographics and dashboards, and featuring 1,948 questions in
various types, such as multiple-choice, conversational, hypothetical, and
unanswerable questions, to better reflect real-world challenges. Our
evaluations with 21 models show a substantial performance drop for LVLMs on
ChartQAPro; e.g., Claude Sonnet 3.5 scores 90.5% on ChartQA but only 55.81% on
ChartQAPro, underscoring the complexity of chart reasoning. We complement our
findings with detailed error analyses and ablation studies, identifying key
challenges and opportunities for advancing LVLMs in chart understanding and
reasoning. We release ChartQAPro at https://github.com/vis-nlp/ChartQAPro.
FreshStack: Building Realistic Benchmarks for Evaluating Retrieval on
  Technical Documents
Authors: name: Nandan Thakur, name: Jimmy Lin, name: Sam Havens, name: Michael Carbin, name: Omar Khattab, name: Andrew Drozdov
Published: 2025-04-17 17:44:06

We introduce FreshStack, a reusable framework for automatically building
information retrieval (IR) evaluation benchmarks from community-asked questions
and answers. FreshStack conducts the following steps: (1) automatic corpus
collection from code and technical documentation, (2) nugget generation from
community-asked questions and answers, and (3) nugget-level support, retrieving
documents using a fusion of retrieval techniques and hybrid architectures. We
use FreshStack to build five datasets on fast-growing, recent, and niche topics
to ensure the tasks are sufficiently challenging. On FreshStack, existing
retrieval models, when applied out-of-the-box, significantly underperform
oracle approaches on all five topics, denoting plenty of headroom to improve IR
quality. In addition, we identify cases where rerankers do not clearly improve
first-stage retrieval accuracy (two out of five topics). We hope that
FreshStack will facilitate future work toward constructing realistic, scalable,
and uncontaminated IR and RAG evaluation benchmarks. FreshStack datasets are
available at: https://fresh-stack.github.io.
MLRC-Bench: Can Language Agents Solve Machine Learning Research
  Challenges?
Authors: name: Yunxiang Zhang, name: Muhammad Khalifa, name: Shitanshu Bhushan, name: Grant D Murphy, name: Lajanugen Logeswaran, name: Jaekyeom Kim, name: Moontae Lee, name: Honglak Lee, name: Lu Wang
Published: 2025-04-13 19:35:43

Existing evaluation of large language model (LLM) agents on scientific
discovery lacks objective baselines and metrics to assess the viability of
their proposed methods. To address this issue, we introduce MLRC-Bench, a
benchmark designed to quantify how effectively language agents can tackle
challenging Machine Learning (ML) Research Competitions. Our benchmark
highlights open research problems that demand novel methodologies, in contrast
to recent benchmarks such as OpenAI's MLE-Bench (Chan et al., 2024) and METR's
RE-Bench (Wijk et al., 2024), which focus on well-established research tasks
that are largely solvable through sufficient engineering effort. Unlike prior
work, e.g., AI Scientist (Lu et al., 2024b), which evaluates the end-to-end
agentic pipeline by using LLM-as-a-judge, MLRC-Bench measures the key steps of
proposing and implementing novel research methods and evaluates them with newly
proposed rigorous protocol and objective metrics. Our curated suite of 7
competition tasks reveals significant challenges for LLM agents. Even the
best-performing tested agent (gemini-exp-1206 under MLAB (Huang et al., 2024a))
closes only 9.3% of the gap between baseline and top human participant scores.
Furthermore, our analysis reveals a misalignment between the LLM-judged
innovation and their actual performance on cutting-edge ML research problems.
MLRC-Bench is a dynamic benchmark, which is designed to continually grow with
new ML competitions to encourage rigorous and objective evaluations of AI's
research capabilities.
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large
  Vision-Language Models
Authors: name: Hardy Chen, name: Haoqin Tu, name: Fali Wang, name: Hui Liu, name: Xianfeng Tang, name: Xinya Du, name: Yuyin Zhou, name: Cihang Xie
Published: 2025-04-10 16:54:05

This work revisits the dominant supervised fine-tuning (SFT) then
reinforcement learning (RL) paradigm for training Large Vision-Language Models
(LVLMs), and reveals a key finding: SFT can significantly undermine subsequent
RL by inducing ``pseudo reasoning paths'' imitated from expert models. While
these paths may resemble the native reasoning paths of RL models, they often
involve prolonged, hesitant, less informative steps, and incorrect reasoning.
To systematically study this effect, we introduce VLAA-Thinking, a new
multimodal dataset designed to support reasoning in LVLMs. Constructed via a
six-step pipeline involving captioning, reasoning distillation, answer rewrite
and verification, VLAA-Thinking comprises high-quality, step-by-step visual
reasoning traces for SFT, along with a more challenging RL split from the same
data source. Using this dataset, we conduct extensive experiments comparing
SFT, RL and their combinations. Results show that while SFT helps models learn
reasoning formats, it often locks aligned models into imitative, rigid
reasoning modes that impede further learning. In contrast, building on the
Group Relative Policy Optimization (GRPO) with a novel mixed reward module
integrating both perception and cognition signals, our RL approach fosters more
genuine, adaptive reasoning behavior. Notably, our model VLAA-Thinker, based on
Qwen2.5VL 3B, achieves top-1 performance on Open LMM Reasoning Leaderboard
(https://huggingface.co/spaces/opencompass/Open_LMM_Reasoning_Leaderboard)
among 4B scale LVLMs, surpassing the previous state-of-the-art by 1.8%. We hope
our findings provide valuable insights in developing reasoning-capable LVLMs
and can inform future research in this area.
"It's not a representation of me": Examining Accent Bias and Digital
  Exclusion in Synthetic AI Voice Services
Authors: name: Shira Michel, name: Sufi Kaur, name: Sarah Elizabeth Gillespie, name: Jeffrey Gleason, name: Christo Wilson, name: Avijit Ghosh
Published: 2025-04-12 21:31:22

Recent advances in artificial intelligence (AI) speech generation and voice
cloning technologies have produced naturalistic speech and accurate voice
replication, yet their influence on sociotechnical systems across diverse
accents and linguistic traits is not fully understood. This study evaluates two
synthetic AI voice services (Speechify and ElevenLabs) through a mixed methods
approach using surveys and interviews to assess technical performance and
uncover how users' lived experiences influence their perceptions of accent
variations in these speech technologies. Our findings reveal technical
performance disparities across five regional, English-language accents and
demonstrate how current speech generation technologies may inadvertently
reinforce linguistic privilege and accent-based discrimination, potentially
creating new forms of digital exclusion. Overall, our study highlights the need
for inclusive design and regulation by providing actionable insights for
developers, policymakers, and organizations to ensure equitable and socially
responsible AI speech technologies.
Towards Learning to Complete Anything in Lidar
Authors: name: Ayca Takmaz, name: Cristiano Saltori, name: Neehar Peri, name: Tim Meinhardt, name: Riccardo de Lutio, name: Laura Leal-Taixé, name: Aljoša Ošep
Published: 2025-04-16 17:21:55

We propose CAL (Complete Anything in Lidar) for Lidar-based shape-completion
in-the-wild. This is closely related to Lidar-based semantic/panoptic scene
completion. However, contemporary methods can only complete and recognize
objects from a closed vocabulary labeled in existing Lidar datasets. Different
to that, our zero-shot approach leverages the temporal context from multi-modal
sensor sequences to mine object shapes and semantic features of observed
objects. These are then distilled into a Lidar-only instance-level completion
and recognition model. Although we only mine partial shape completions, we find
that our distilled model learns to infer full object shapes from multiple such
partial observations across the dataset. We show that our model can be prompted
on standard benchmarks for Semantic and Panoptic Scene Completion, localize
objects as (amodal) 3D bounding boxes, and recognize objects beyond fixed class
vocabularies. Our project page is
https://research.nvidia.com/labs/dvl/projects/complete-anything-lidar
BlockGaussian: Efficient Large-Scale Scene Novel View Synthesis via
  Adaptive Block-Based Gaussian Splatting
Authors: name: Yongchang Wu, name: Zipeng Qi, name: Zhenwei Shi, name: Zhengxia Zou
Published: 2025-04-12 02:05:55

The recent advancements in 3D Gaussian Splatting (3DGS) have demonstrated
remarkable potential in novel view synthesis tasks. The divide-and-conquer
paradigm has enabled large-scale scene reconstruction, but significant
challenges remain in scene partitioning, optimization, and merging processes.
This paper introduces BlockGaussian, a novel framework incorporating a
content-aware scene partition strategy and visibility-aware block optimization
to achieve efficient and high-quality large-scale scene reconstruction.
Specifically, our approach considers the content-complexity variation across
different regions and balances computational load during scene partitioning,
enabling efficient scene reconstruction. To tackle the supervision mismatch
issue during independent block optimization, we introduce auxiliary points
during individual block optimization to align the ground-truth supervision,
which enhances the reconstruction quality. Furthermore, we propose a
pseudo-view geometry constraint that effectively mitigates rendering
degradation caused by airspace floaters during block merging. Extensive
experiments on large-scale scenes demonstrate that our approach achieves
state-of-the-art performance in both reconstruction efficiency and rendering
quality, with a 5x speedup in optimization and an average PSNR improvement of
1.21 dB on multiple benchmarks. Notably, BlockGaussian significantly reduces
computational requirements, enabling large-scale scene reconstruction on a
single 24GB VRAM device. The project page is available at
https://github.com/SunshineWYC/BlockGaussian
Syzygy of Thoughts: Improving LLM CoT with the Minimal Free Resolution
Authors: name: Chenghao Li, name: Chaoning Zhang, name: Yi Lu, name: Jiaquan Zhang, name: Qigan Sun, name: Xudong Wang, name: Jiwei Wei, name: Guoqing Wang, name: Yang Yang, name: Heng Tao Shen
Published: 2025-04-13 13:35:41

Chain-of-Thought (CoT) prompting enhances the reasoning of large language
models (LLMs) by decomposing problems into sequential steps, mimicking human
logic and reducing errors. However, complex tasks with vast solution spaces and
vague constraints often exceed the capacity of a single reasoning chain.
Inspired by Minimal Free Resolution (MFR) in commutative algebra and algebraic
geometry, we propose Syzygy of Thoughts (SoT)-a novel framework that extends
CoT by introducing auxiliary, interrelated reasoning paths. SoT captures deeper
logical dependencies, enabling more robust and structured problem-solving. MFR
decomposes a module into a sequence of free modules with minimal rank,
providing a structured analytical approach to complex systems. This method
introduces the concepts of "Module", "Betti numbers","Freeness", "Mapping",
"Exactness" and "Minimality", enabling the systematic decomposition of the
original complex problem into logically complete minimal subproblems while
preserving key problem features and reducing reasoning length. We tested SoT
across diverse datasets (e.g., GSM8K, MATH) and models (e.g., GPT-4o-mini,
Qwen2.5), achieving inference accuracy that matches or surpasses mainstream
CoTs standards. Additionally, by aligning the sampling process with algebraic
constraints, our approach enhances the scalability of inference time in LLMs,
ensuring both transparent reasoning and high performance. Our code will be
publicly available at https://github.com/dlMARiA/Syzygy-of-thoughts.