Orionnnn/hub-stats huggingface dataset:
Verifier-free Test-Time Sampling for Vision Language Action Models
Authors: name: Suhyeok Jang, name: Dongyoung Kim, name: Changyeon Kim, name: Youngsuk Kim, name: Jinwoo Shin
Published: 2025-10-07 08:38:08

Vision-Language-Action models (VLAs) have demonstrated remarkable performance
in robot control. However, they remain fundamentally limited in tasks that
require high precision due to their single-inference paradigm. While test-time
scaling approaches using external verifiers have shown promise, they require
additional training and fail to generalize to unseen conditions. We propose
Masking Distribution Guided Selection (MG-Select), a novel test-time scaling
framework for VLAs that leverages the model's internal properties without
requiring additional training or external modules. Our approach utilizes KL
divergence from a reference action token distribution as a confidence metric
for selecting the optimal action from multiple candidates. We introduce a
reference distribution generated by the same VLA but with randomly masked
states and language conditions as inputs, ensuring maximum uncertainty while
remaining aligned with the target task distribution. Additionally, we propose a
joint training strategy that enables the model to learn both conditional and
unconditional distributions by applying dropout to state and language
conditions, thereby further improving the quality of the reference
distribution. Our experiments demonstrate that MG-Select achieves significant
performance improvements, including a 28%/35% improvement in real-world
in-distribution/out-of-distribution tasks, along with a 168% relative gain on
RoboCasa pick-and-place tasks trained with 30 demonstrations.
Less is More: Recursive Reasoning with Tiny Networks
Authors: name: Alexia Jolicoeur-Martineau
Published: 2025-10-06 14:58:08

Hierarchical Reasoning Model (HRM) is a novel approach using two small neural
networks recursing at different frequencies. This biologically inspired method
beats Large Language models (LLMs) on hard puzzle tasks such as Sudoku, Maze,
and ARC-AGI while trained with small models (27M parameters) on small data
(around 1000 examples). HRM holds great promise for solving hard problems with
small networks, but it is not yet well understood and may be suboptimal. We
propose Tiny Recursive Model (TRM), a much simpler recursive reasoning approach
that achieves significantly higher generalization than HRM, while using a
single tiny network with only 2 layers. With only 7M parameters, TRM obtains
45% test-accuracy on ARC-AGI-1 and 8% on ARC-AGI-2, higher than most LLMs
(e.g., Deepseek R1, o3-mini, Gemini 2.5 Pro) with less than 0.01% of the
parameters.
Presenting a Paper is an Art: Self-Improvement Aesthetic Agents for
  Academic Presentations
Authors: name: Chengzhi Liu, name: Yuzhe Yang, name: Kaiwen Zhou, name: Zhen Zhang, name: Yue Fan, name: Yannan Xie, name: Peng Qi, name: Xin Eric Wang
Published: 2025-10-07 04:24:26

The promotion of academic papers has become an important means of enhancing
research visibility. However, existing automated methods struggle limited
storytelling, insufficient aesthetic quality, and constrained self-adjustment,
making it difficult to achieve efficient and engaging dissemination. At the
heart of those challenges is a simple principle: there is no way to
improve it when you cannot evaluate it right. To address this, we introduce
EvoPresent, a self-improvement agent framework that unifies coherent
narratives, aesthetic-aware designs, and realistic presentation delivery via
virtual characters. Central to EvoPresent is PresAesth, a multi-task
reinforcement learning (RL) aesthetic model that provides reliable aesthetic
scoring, defect adjustment, and comparative feedback, enabling iterative
self-improvement even under limited aesthetic training data. To systematically
evaluate the methods, we introduce EvoPresent Benchmark, a
comprehensive benchmark comprising: Presentation Generation Quality,
built on 650 top-tier AI conference papers with multimodal resources (slides,
videos and scripts) to assess both content and design; and Aesthetic
Awareness, consisting of 2,000 slide pairs with varying aesthetic levels,
supporting joint training and evaluation on scoring, defect adjustment, and
comparison. Our findings highlight that (i) High-quality feedback is essential
for agent self-improvement, while initial capability alone does not guarantee
effective self-correction. (ii) Automated generation pipelines exhibit a
trade-off between visual design and content construction. (iii) Multi-task RL
training shows stronger generalization in aesthetic awareness tasks.
Drax: Speech Recognition with Discrete Flow Matching
Authors: name: Aviv Navon, name: Aviv Shamsian, name: Neta Glazer, name: Yael Segal-Feldman, name: Gill Hetz, name: Joseph Keshet, name: Ethan Fetaya
Published: 2025-10-05 11:38:01

Diffusion and flow-based non-autoregressive (NAR) models have shown strong
promise in large language modeling, however, their potential for automatic
speech recognition (ASR) remains largely unexplored. We propose Drax, a
discrete flow matching framework for ASR that enables efficient parallel
decoding. To better align training with inference, we construct an
audio-conditioned probability path that guides the model through trajectories
resembling likely intermediate inference errors, rather than direct random
noise to target transitions. Our theoretical analysis links the generalization
gap to divergences between training and inference occupancies, controlled by
cumulative velocity errors, thereby motivating our design choice. Empirical
evaluation demonstrates that our approach attains recognition accuracy on par
with state-of-the-art speech models while offering improved accuracy-efficiency
trade-offs, highlighting discrete flow matching as a promising direction for
advancing NAR ASR.
ShapeGen4D: Towards High Quality 4D Shape Generation from Videos
Authors: name: Jiraphon Yenphraphai, name: Ashkan Mirzaei, name: Jianqi Chen, name: Jiaxu Zou, name: Sergey Tulyakov, name: Raymond A. Yeh, name: Peter Wonka, name: Chaoyang Wang
Published: 2025-10-07 17:58:11

Video-conditioned 4D shape generation aims to recover time-varying 3D
geometry and view-consistent appearance directly from an input video. In this
work, we introduce a native video-to-4D shape generation framework that
synthesizes a single dynamic 3D representation end-to-end from the video. Our
framework introduces three key components based on large-scale pre-trained 3D
models: (i) a temporal attention that conditions generation on all frames while
producing a time-indexed dynamic representation; (ii) a time-aware point
sampling and 4D latent anchoring that promote temporally consistent geometry
and texture; and (iii) noise sharing across frames to enhance temporal
stability. Our method accurately captures non-rigid motion, volume changes, and
even topological transitions without per-frame optimization. Across diverse
in-the-wild videos, our method improves robustness and perceptual fidelity and
reduces failure modes compared with the baselines.
In-the-Flow Agentic System Optimization for Effective Planning and Tool
  Use
Authors: name: Zhuofeng Li, name: Haoxiang Zhang, name: Seungju Han, name: Sheng Liu, name: Jianwen Xie, name: Yu Zhang, name: Yejin Choi, name: James Zou, name: Pan Lu
Published: 2025-10-07 05:32:44

Outcome-driven reinforcement learning has advanced reasoning in large
language models (LLMs), but prevailing tool-augmented approaches train a
single, monolithic policy that interleaves thoughts and tool calls under full
context; this scales poorly with long horizons and diverse tools and
generalizes weakly to new scenarios. Agentic systems offer a promising
alternative by decomposing work across specialized modules, yet most remain
training-free or rely on offline training decoupled from the live dynamics of
multi-turn interaction. We introduce AgentFlow, a trainable, in-the-flow
agentic framework that coordinates four modules (planner, executor, verifier,
generator) through an evolving memory and directly optimizes its planner inside
the multi-turn loop. To train on-policy in live environments, we propose
Flow-based Group Refined Policy Optimization (Flow-GRPO), which tackles
long-horizon, sparse-reward credit assignment by converting multi-turn
optimization into a sequence of tractable single-turn policy updates. It
broadcasts a single, verifiable trajectory-level outcome to every turn to align
local planner decisions with global success and stabilizes learning with
group-normalized advantages. Across ten benchmarks, AgentFlow with a 7B-scale
backbone outperforms top-performing baselines with average accuracy gains of
14.9% on search, 14.0% on agentic, 14.5% on mathematical, and 4.1% on
scientific tasks, even surpassing larger proprietary models like GPT-4o.
Further analyses confirm the benefits of in-the-flow optimization, showing
improved planning, enhanced tool-calling reliability, and positive scaling with
model size and reasoning turns.
Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI
  Models for Scatterplot-Related Tasks
Authors: name: João Palmeiro, name: Diogo Duarte, name: Rita Costa, name: Pedro Bizarro
Published: 2025-10-07 15:59:19

AI models are increasingly used for data analysis and visualization, yet
benchmarks rarely address scatterplot-specific tasks, limiting insight into
performance. To address this gap for one of the most common chart types, we
introduce a synthetic, annotated dataset of over 18,000 scatterplots from six
data generators and 17 chart designs, and a benchmark based on it. We evaluate
proprietary models from OpenAI and Google using N-shot prompting on five
distinct tasks derived from annotations of cluster bounding boxes, their center
coordinates, and outlier coordinates. OpenAI models and Gemini 2.5 Flash,
especially when prompted with examples, are viable options for counting
clusters and, in Flash's case, outliers (90%+ Accuracy). However, the results
for localization-related tasks are unsatisfactory: Precision and Recall are
near or below 50%, except for Flash in outlier identification (65.01%).
Furthermore, the impact of chart design on performance appears to be a
secondary factor, but it is advisable to avoid scatterplots with wide aspect
ratios (16:9 and 21:9) or those colored randomly. Supplementary materials are
available at https://github.com/feedzai/biy-paper.
CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical
  Contrastive Decoding
Authors: name: Xi Zhang, name: Zaiqiao Meng, name: Jake Lever, name: Edmond S. L. Ho
Published: 2025-09-27 16:01:09

Multimodal large language models (MLLMs) have recently achieved remarkable
progress in radiology by integrating visual perception with natural language
understanding. However, they often generate clinically unsupported
descriptions, known as medical hallucinations, which pose serious risks in
medical applications that demand accuracy and image-grounded outputs. Through
empirical analysis, we find that prompt-induced hallucinations remain prevalent
in radiology MLLMs, largely due to over-sensitivity to clinical sections. To
address this, we introduce Clinical Contrastive Cecoding (CCD), a training-free
and retrieval-free inference framework that integrates structured clinical
signals from task-specific radiology expert models. CCD introduces a dual-stage
contrastive mechanism to refine token-level logits during generation, thereby
enhancing clinical fidelity without modifying the base MLLM. Experiments on
three datasets and multiple models demonstrate that CCD consistently improves
overall performance on radiology report generation (RRG). On the MIMIC-CXR
dataset, it yields up to a 17% improvement in RadGraph-F1 when applied to
state-of-the-art RRG models. Our approach provides a lightweight and
generalisable solution for mitigating medical hallucinations, effectively
bridging expert models and MLLMs in radiology.
Training Dynamics Impact Post-Training Quantization Robustness
Authors: name: Albert Catalan-Tatjer, name: Niccolò Ajroldi, name: Jonas Geiping
Published: 2025-10-07 17:59:07

While post-training quantization is widely adopted for efficient deployment
of large language models, the mechanisms underlying quantization robustness
remain unclear. We conduct a comprehensive analysis of quantization degradation
across open-source language model training trajectories up to 32B parameters
and 15T training tokens to accurately assess the relationship between training
dynamics and quantization performance. Our key finding is that quantization
errors in large-scale training runs are driven by a complex interplay between
learning rate and other training hyperparameters. Specifically, once learning
rates decay, validation loss and quantization error diverge, largely
independent of training data scale. To investigate interventions on the
training dynamics and identify specific configurations that can modulate
quantization robustness favorably, we train our own models in controlled
experiments up to 100B tokens. Our results challenge the assumption that
increasing dataset scale inherently compromises quantization effectiveness,
demonstrating instead that strategic training hyperparameter interventions can
improve quantization quality at scale.
OneFlow: Concurrent Mixed-Modal and Interleaved Generation with Edit
  Flows
Authors: name: John Nguyen, name: Marton Havasi, name: Tariq Berrada, name: Luke Zettlemoyer, name: Ricky T. Q. Chen
Published: 2025-10-03 20:40:30

We present OneFlow, the first non-autoregressive multimodal model that
enables variable-length and concurrent mixed-modal generation. Unlike
autoregressive models that enforce rigid causal ordering between text and image
generation, OneFlow combines an insertion-based Edit Flow for discrete text
tokens with Flow Matching for image latents. OneFlow enables concurrent
text-image synthesis with hierarchical sampling that prioritizes content over
grammar. Through controlled experiments across model sizes from 1B to 8B, we
demonstrate that OneFlow outperforms autoregressive baselines on both
generation and understanding tasks while using up to 50% fewer training FLOPs.
OneFlow surpasses both autoregressive and diffusion-based approaches while
unlocking new capabilities for concurrent generation, iterative refinement, and
natural reasoning-like generation.
Equilibrium Matching: Generative Modeling with Implicit Energy-Based
  Models
Authors: name: Runqian Wang, name: Yilun Du
Published: 2025-10-02 17:59:06

We introduce Equilibrium Matching (EqM), a generative modeling framework
built from an equilibrium dynamics perspective. EqM discards the
non-equilibrium, time-conditional dynamics in traditional diffusion and
flow-based generative models and instead learns the equilibrium gradient of an
implicit energy landscape. Through this approach, we can adopt an
optimization-based sampling process at inference time, where samples are
obtained by gradient descent on the learned landscape with adjustable step
sizes, adaptive optimizers, and adaptive compute. EqM surpasses the generation
performance of diffusion/flow models empirically, achieving an FID of 1.90 on
ImageNet 256times256. EqM is also theoretically justified to learn and
sample from the data manifold. Beyond generation, EqM is a flexible framework
that naturally handles tasks including partially noised image denoising, OOD
detection, and image composition. By replacing time-conditional velocities with
a unified equilibrium landscape, EqM offers a tighter bridge between flow and
energy-based models and a simple route to optimization-driven inference.
TensorBLEU: Vectorized GPU-based BLEU Score Implementation for
  Per-Sentence In-Training Evaluation
Authors: name: Adam Filipek
Published: 2025-10-07 01:02:46

Modern natural language processing models have achieved unprecedented scale,
yet the tools for their evaluation often remain a computational bottleneck,
limiting the pace of research. This is particularly acute for in-training
evaluation metrics, such as per-sentence reward signals in Reinforcement
Learning, which must operate efficiently on batches of token IDs directly on
the GPU. In this paper, we introduce TensorBLEU, a novel implementation of the
BLEU metric designed from the ground up for this specific use case. Our
approach is fully vectorized for GPU-accelerated, per-sentence computation
within PyTorch and introduces a memory-efficient counting mechanism. By
creating a compact, batch-specific dictionary of n-grams using
torch.unique, our method avoids the prohibitive memory costs of
traditional hashing-based vectorization, making it practical for
large-vocabulary models. We benchmark TensorBLEU against NLTK, the standard
library for token-ID-based BLEU calculation on the CPU. Experiments show that
TensorBLEU provides speedups of over 13x on consumer-grade GPUs (NVIDIA T4) and
exceeding 40x on data-center-class hardware (NVIDIA A100). This performance
transforms a significant bottleneck into a negligible part of the training
loop. By clearly defining its role as a "Token-ID BLEU" for development
purposes and open-sourcing our implementation, we provide a powerful tool for
accelerating research in areas like RL-based model fine-tuning.
BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation for Large Language
  Models via Lens of Dynamic Interactions
Authors: name: Nan Huo, name: Xiaohan Xu, name: Jinyang Li, name: Per Jacobsson, name: Shipei Lin, name: Bowen Qin, name: Binyuan Hui, name: Xiaolong Li, name: Ge Qu, name: Shuzheng Si, name: Linheng Han, name: Edward Alexander, name: Xintong Zhu, name: Rui Qin, name: Ruihan Yu, name: Yiyao Jin, name: Feige Zhou, name: Weihao Zhong, name: Yun Chen, name: Hongyu Liu, name: Chenhao Ma, name: Fatma Ozcan, name: Yannis Papakonstantinou, name: Reynold Cheng
Published: 2025-10-06 19:31:47

Large language models (LLMs) have demonstrated remarkable performance on
single-turn text-to-SQL tasks, but real-world database applications
predominantly require multi-turn interactions to handle ambiguous queries,
execution errors, and evolving user requirements. Existing multi-turn
benchmarks fall short by treating conversation histories as static context or
limiting evaluation to read-only operations, failing to reflect
production-grade database assistant challenges. We introduce BIRD-INTERACT, a
benchmark that restores this realism through: (1) a comprehensive interaction
environment coupling each database with a hierarchical knowledge base, metadata
files, and a function-driven user simulator, enabling models to solicit
clarifications, retrieve knowledge, and recover from errors without human
supervision; (2) two evaluation settings consisting of a pre-defined
conversational protocol (c-Interact) and an open-ended agentic setting
(a-Interact) where models autonomously decide when to query the user simulator
or explore the environment; (3) a challenging task suite covering the full CRUD
spectrum for business-intelligence and operational use cases, guarded by
executable test cases. Each task features ambiguous and follow-up sub-tasks
requiring dynamic interaction. The suite comprises BIRD-INTERACT-FULL (600
tasks, up to 11,796 interactions) for comprehensive performance assessment, and
BIRD-INTERACT-LITE (300 tasks with simplified databases) for detailed
behavioral analysis and rapid method development. Our empirical results
highlight BIRD-INTERACT's difficulty: GPT-5 completes only 8.67% of tasks in
c-Interact and 17.00% in a-Interact. Analysis via memory grafting and
Interaction Test-time Scaling validates the importance of effective interaction
for complex, dynamic text-to-SQL tasks.
Revisiting Modeling and Evaluation Approaches in Speech Emotion
  Recognition: Considering Subjectivity of Annotators and Ambiguity of Emotions
Authors: name: Huang-Cheng Chou, name: Chi-Chun Lee
Published: 2025-10-07 13:45:09

Over the past two decades, speech emotion recognition (SER) has received
growing attention. To train SER systems, researchers collect emotional speech
databases annotated by crowdsourced or in-house raters who select emotions from
predefined categories. However, disagreements among raters are common.
Conventional methods treat these disagreements as noise, aggregating labels
into a single consensus target. While this simplifies SER as a single-label
task, it ignores the inherent subjectivity of human emotion perception. This
dissertation challenges such assumptions and asks: (1) Should minority
emotional ratings be discarded? (2) Should SER systems learn from only a few
individuals' perceptions? (3) Should SER systems predict only one emotion per
sample?
  Psychological studies show that emotion perception is subjective and
ambiguous, with overlapping emotional boundaries. We propose new modeling and
evaluation perspectives: (1) Retain all emotional ratings and represent them
with soft-label distributions. Models trained on individual annotator ratings
and jointly optimized with standard SER systems improve performance on
consensus-labeled tests. (2) Redefine SER evaluation by including all emotional
data and allowing co-occurring emotions (e.g., sad and angry). We propose an
``all-inclusive rule'' that aggregates all ratings to maximize diversity in
label representation. Experiments on four English emotion databases show
superior performance over majority and plurality labeling. (3) Construct a
penalization matrix to discourage unlikely emotion combinations during
training. Integrating it into loss functions further improves performance.
Overall, embracing minority ratings, multiple annotators, and multi-emotion
predictions yields more robust and human-aligned SER systems.
Distributional Semantics Tracing: A Framework for Explaining
  Hallucinations in Large Language Models
Authors: name: Gagan Bhatia, name: Somayajulu G Sripada, name: Kevin Allan, name: Jacobo Azcona
Published: 2025-10-07 16:40:31

Large Language Models (LLMs) are prone to hallucination, the generation of
plausible yet factually incorrect statements. This work investigates the
intrinsic, architectural origins of this failure mode through three primary
contributions.First, to enable the reliable tracing of internal semantic
failures, we propose Distributional Semantics Tracing (DST), a unified
framework that integrates established interpretability techniques to produce a
causal map of a model's reasoning, treating meaning as a function of context
(distributional semantics). Second, we pinpoint the model's layer at which a
hallucination becomes inevitable, identifying a specific commitment
layer where a model's internal representations irreversibly diverge from
factuality. Third, we identify the underlying mechanism for these failures. We
observe a conflict between distinct computational pathways, which we interpret
using the lens of dual-process theory: a fast, heuristic associative
pathway (akin to System 1) and a slow, deliberate contextual pathway
(akin to System 2), leading to predictable failure modes such as
Reasoning Shortcut Hijacks. Our framework's ability to quantify the
coherence of the contextual pathway reveals a strong negative correlation
(rho = -0.863) with hallucination rates, implying that these failures are
predictable consequences of internal semantic weakness. The result is a
mechanistic account of how, when, and why hallucinations occur within the
Transformer architecture.