Orionnnn/hub-stats huggingface dataset:
CLUE: Non-parametric Verification from Experience via Hidden-State
  Clustering
Authors: name: Zhenwen Liang, name: Ruosen Li, name: Yujun Zhou, name: Linfeng Song, name: Dian Yu, name: Xinya Du, name: Haitao Mi, name: Dong Yu
Published: 2025-10-02 02:14:33

Assessing the quality of Large Language Model (LLM) outputs presents a
critical challenge. Previous methods either rely on text-level information
(e.g., reward models, majority voting), which can overfit to superficial cues,
or on calibrated confidence from token probabilities, which would fail on
less-calibrated models. Yet both of these signals are, in fact, partial
projections of a richer source of information: the model's internal hidden
states. Early layers, closer to token embeddings, preserve semantic and lexical
features that underpin text-based judgments, while later layers increasingly
align with output logits, embedding confidence-related information. This paper
explores hidden states directly as a unified foundation for verification. We
show that the correctness of a solution is encoded as a geometrically separable
signature within the trajectory of hidden activations. To validate this, we
present Clue (Clustering and Experience-based Verification), a deliberately
minimalist, non-parametric verifier. With no trainable parameters, CLUE only
summarizes each reasoning trace by an hidden state delta and classifies
correctness via nearest-centroid distance to ``success'' and ``failure''
clusters formed from past experience. The simplicity of this method highlights
the strength of the underlying signal. Empirically, CLUE consistently
outperforms LLM-as-a-judge baselines and matches or exceeds modern
confidence-based methods in reranking candidates, improving both top-1 and
majority-vote accuracy across AIME 24/25 and GPQA. As a highlight, on AIME 24
with a 1.5B model, CLUE boosts accuracy from 56.7% (majority@64) to 70.0%
(top-maj@16).
AReUReDi: Annealed Rectified Updates for Refining Discrete Flows with
  Multi-Objective Guidance
Authors: name: Tong Chen, name: Yinuo Zhang, name: Pranam Chatterjee
Published: 2025-09-30 23:33:33

Designing sequences that satisfy multiple, often conflicting, objectives is a
central challenge in therapeutic and biomolecular engineering. Existing
generative frameworks largely operate in continuous spaces with
single-objective guidance, while discrete approaches lack guarantees for
multi-objective Pareto optimality. We introduce AReUReDi (Annealed Rectified
Updates for Refining Discrete Flows), a discrete optimization algorithm with
theoretical guarantees of convergence to the Pareto front. Building on
Rectified Discrete Flows (ReDi), AReUReDi combines Tchebycheff scalarization,
locally balanced proposals, and annealed Metropolis-Hastings updates to bias
sampling toward Pareto-optimal states while preserving distributional
invariance. Applied to peptide and SMILES sequence design, AReUReDi
simultaneously optimizes up to five therapeutic properties (including affinity,
solubility, hemolysis, half-life, and non-fouling) and outperforms both
evolutionary and diffusion-based baselines. These results establish AReUReDi as
a powerful, sequence-based framework for multi-property biomolecule generation.
Pay-Per-Search Models are Abstention Models
Authors: name: Mustafa Omer Gul, name: Claire Cardie, name: Tanya Goyal
Published: 2025-10-01 17:41:54

LLMs cannot reliably recognize their parametric knowledge boundaries and
often hallucinate answers to outside-of-boundary questions. In contrast, humans
recognize their limitations and can either seek external help for such
questions or abstain. In this paper, we introduce MASH (Modeling Abstention via
Selective Help-seeking), a training framework that readily extracts abstentions
from LLMs. Our key idea is that any external help-seeking by an LLM, i.e.
search tool use, can serve as a proxy for abstention if the external help
(search) is appropriately penalized while simultaneously rewarding answer
accuracy. MASH operationalizes this idea using reinforcement learning with a
pay-per-search reward.
  We run experiments on three knowledge-intensive QA datasets. Our results show
that MASH substantially improves upon the selective help-seeking performance of
prior efficient search approaches; on multi-hop datasets, MASH improves answer
accuracy by 7.6%. Furthermore, MASH demonstrates strong off-the-shelf
abstention -- it can distinguish between unanswerable/answerable questions and
selectively generate responses for answerable questions -- showcasing behavior
analogous to specialized abstention approaches. We emphasize that contrary to
prior abstention methods, MASH does not require pre-determining knowledge
boundaries to construct training data. Instead, MASH's abstentions are a
by-product of training for the auxiliary selective help-seeking task. Overall,
we show that MASH training effectively aligns search tool use with parametric
knowledge, which can be successfully leveraged for making abstention decisions.
BatonVoice: An Operationalist Framework for Enhancing Controllable
  Speech Synthesis with Linguistic Intelligence from LLMs
Authors: name: Yue Wang, name: Ruotian Ma, name: Xingyu Chen, name: Zhengliang Shi, name: Wanshun Chen, name: Huang Liu, name: Jiadi Yao, name: Qu Yang, name: Qingxuan Jiang, name: Fanghua Ye, name: Juntao Li, name: Min Zhang, name: Zhaopeng Tu, name: Xiaolong Li, name: Linus
Published: 2025-09-30 16:52:14

The rise of Large Language Models (LLMs) is reshaping multimodel models, with
speech synthesis being a prominent application. However, existing approaches
often underutilize the linguistic intelligence of these models, typically
failing to leverage their powerful instruction-following capabilities. This
limitation hinders the model's ability to follow text instructions for
controllable Text-to-Speech~(TTS). To address this, we propose a new paradigm
inspired by ``operationalism'' that decouples instruction understanding from
speech generation. We introduce BatonVoice, a framework where an LLM acts as a
``conductor'', understanding user instructions and generating a textual
``plan'' -- explicit vocal features (e.g., pitch, energy). A separate TTS
model, the ``orchestra'', then generates the speech from these features. To
realize this component, we develop BatonTTS, a TTS model trained specifically
for this task. Our experiments demonstrate that BatonVoice achieves strong
performance in controllable and emotional speech synthesis, outperforming
strong open- and closed-source baselines. Notably, our approach enables
remarkable zero-shot cross-lingual generalization, accurately applying feature
control abilities to languages unseen during post-training. This demonstrates
that objectifying speech into textual vocal features can more effectively
unlock the linguistic intelligence of LLMs.
Aligning Visual Foundation Encoders to Tokenizers for Diffusion Models
Authors: name: Bowei Chen, name: Sai Bi, name: Hao Tan, name: He Zhang, name: Tianyuan Zhang, name: Zhengqi Li, name: Yuanjun Xiong, name: Jianming Zhang, name: Kai Zhang
Published: 2025-09-29 17:57:39

In this work, we propose aligning pretrained visual encoders to serve as
tokenizers for latent diffusion models in image generation. Unlike training a
variational autoencoder (VAE) from scratch, which primarily emphasizes
low-level details, our approach leverages the rich semantic structure of
foundation encoders. We introduce a three-stage alignment strategy: (1) freeze
the encoder and train an adapter and a decoder to establish a semantic latent
space; (2) jointly optimize all components with an additional semantic
preservation loss, enabling the encoder to capture perceptual details while
retaining high-level semantics; and (3) refine the decoder for improved
reconstruction quality. This alignment yields semantically rich image
tokenizers that benefit diffusion models. On ImageNet 256times256, our
tokenizer accelerates the convergence of diffusion models, reaching a gFID of
1.90 within just 64 epochs, and improves generation both with and without
classifier-free guidance. Scaling to LAION, a 2B-parameter text-to-image model
trained with our tokenizer consistently outperforms FLUX VAE under the same
training steps. Overall, our method is simple, scalable, and establishes a
semantically grounded paradigm for continuous tokenizer design.
MixtureVitae: Open Web-Scale Pretraining Dataset With High Quality
  Instruction and Reasoning Data Built from Permissive-First Text Sources
Authors: name: Huu Nguyen, name: Victor May, name: Harsh Raj, name: Marianna Nezhurina, name: Yishan Wang, name: Yanqi Luo, name: Minh Chien Vu, name: Taishi Nakamura, name: Ken Tsui, name: Van Khue Nguyen, name: David Salinas, name: Aleksandra Krasnodębska, name: Christoph Schuhmann, name: Mats Leon Richter, name: Xuan-Son, name: Vu, name: Jenia Jitsev
Published: 2025-09-29 21:40:10

We present MixtureVitae, an open-access pretraining corpus built to minimize
legal risk while providing strong model performance. MixtureVitae follows a
risk-mitigated sourcing strategy that combines public-domain and permissively
licensed text (e.g., CC-BY/Apache) with carefully justified low-risk additions
(e.g., government works and EU TDM-eligible sources), alongside targeted
instruction, reasoning and synthetic data with documented provenance. We detail
a transparent, multi-stage pipeline for license-aware filtering, safety and
quality screening, and domain-aware mixing, and we release the dataset and
curation recipes to support reproducible research. In controlled experiments
using the open-sci-ref training protocol (fixed architectures at
130M/400M/1.3B/1.7B parameters; training budgets of 50B and 300B tokens),
models trained on MixtureVitae consistently outperform other permissive
datasets across a suite of standard benchmarks, and at the 1.7B/300B setting
they surpass FineWeb-Edu and approach DCLM in the later stages of training.
Performance is particularly strong on math/code and competitive on QA tasks.
These results demonstrate that permissive-first, risk-mitigated data provides a
practical and legally mitigated foundation for training capable LLMs, reducing
reliance on indiscriminate web scraping without sacrificing competitiveness.
Code: https://github.com/ontocord/mixturevitae
TGPO: Temporal Grounded Policy Optimization for Signal Temporal Logic
  Tasks
Authors: name: Yue Meng, name: Fei Chen, name: Chuchu Fan
Published: 2025-09-30 19:51:05

Learning control policies for complex, long-horizon tasks is a central
challenge in robotics and autonomous systems. Signal Temporal Logic (STL)
offers a powerful and expressive language for specifying such tasks, but its
non-Markovian nature and inherent sparse reward make it difficult to be solved
via standard Reinforcement Learning (RL) algorithms. Prior RL approaches focus
only on limited STL fragments or use STL robustness scores as sparse terminal
rewards. In this paper, we propose TGPO, Temporal Grounded Policy Optimization,
to solve general STL tasks. TGPO decomposes STL into timed subgoals and
invariant constraints and provides a hierarchical framework to tackle the
problem. The high-level component of TGPO proposes concrete time allocations
for these subgoals, and the low-level time-conditioned policy learns to achieve
the sequenced subgoals using a dense, stage-wise reward signal. During
inference, we sample various time allocations and select the most promising
assignment for the policy network to rollout the solution trajectory. To foster
efficient policy learning for complex STL with multiple subgoals, we leverage
the learned critic to guide the high-level temporal search via
Metropolis-Hastings sampling, focusing exploration on temporally feasible
solutions. We conduct experiments on five environments, ranging from
low-dimensional navigation to manipulation, drone, and quadrupedal locomotion.
Under a wide range of STL tasks, TGPO significantly outperforms
state-of-the-art baselines (especially for high-dimensional and long-horizon
cases), with an average of 31.6% improvement in task success rate compared to
the best baseline. The code will be available at
https://github.com/mengyuest/TGPO
EditReward: A Human-Aligned Reward Model for Instruction-Guided Image
  Editing
Authors: name: Keming Wu, name: Sicong Jiang, name: Max Ku, name: Ping Nie, name: Minghao Liu, name: Wenhu Chen
Published: 2025-09-30 14:51:04

Recently, we have witnessed great progress in image editing with natural
language instructions. Several closed-source models like GPT-Image-1, Seedream,
and Google-Nano-Banana have shown highly promising progress. However, the
open-source models are still lagging. The main bottleneck is the lack of a
reliable reward model to scale up high-quality synthetic training data. To
address this critical bottleneck, we built \mname, trained with our new
large-scale human preference dataset, meticulously annotated by trained experts
following a rigorous protocol containing over 200K preference pairs. \mname
demonstrates superior alignment with human preferences in instruction-guided
image editing tasks. Experiments show that \mname achieves state-of-the-art
human correlation on established benchmarks such as GenAI-Bench, AURORA-Bench,
ImagenHub, and our new \benchname, outperforming a wide range of VLM-as-judge
models. Furthermore, we use \mname to select a high-quality subset from the
existing noisy ShareGPT-4o-Image dataset. We train Step1X-Edit on the selected
subset, which shows significant improvement over training on the full set. This
demonstrates \mname's ability to serve as a reward model to scale up
high-quality training data for image editing. Furthermore, its strong alignment
suggests potential for advanced applications like reinforcement learning-based
post-training and test-time scaling of image editing models. \mname with its
training dataset will be released to help the community build more high-quality
image editing training datasets.
SINQ: Sinkhorn-Normalized Quantization for Calibration-Free
  Low-Precision LLM Weights
Authors: name: Lorenz K. Müller, name: Philippe Bich, name: Jiawei Zhuang, name: Ahmet Çelik, name: Luca Benfenati, name: Lukas Cavigelli
Published: 2025-09-26 21:22:54

Post-training quantization has emerged as the most widely used strategy for
deploying large language models at low precision. Still, current methods show
perplexity degradation at bit-widths less than or equal to 4, partly because
representing outliers causes precision issues in parameters that share the same
scales as these outliers. This problem is especially pronounced for
calibration-free, uniform quantization methods. We introduce SINQ to augment
existing post-training quantizers with an additional second-axis scale factor
and a fast Sinkhorn-Knopp-style algorithm that finds scales to normalize
per-row and per-column variances, thereby minimizing a novel per-matrix proxy
target for quantization: the matrix imbalance. Our method has no interactions
between layers and can be trivially applied to new architectures to quantize
any linear layers. We evaluate our method on the Qwen3 model family and
DeepSeek-V2.5. SINQ improves WikiText2 and C4 perplexity significantly against
uncalibrated uniform quantization baselines and can be further enhanced by
combining it with calibration and non-uniform quantization levels. Code to
reproduce the results of this work and to easily quantize models using SINQ is
available at https://github.com/huawei-csl/SINQ.
QUASAR: Quantum Assembly Code Generation Using Tool-Augmented LLMs via
  Agentic RL
Authors: name: Cong Yu, name: Valter Uotila, name: Shilong Deng, name: Qingyuan Wu, name: Tuo Shi, name: Songlin Jiang, name: Lei You, name: Bo Zhao
Published: 2025-10-01 14:40:04

Designing and optimizing task-specific quantum circuits are crucial to
leverage the advantage of quantum computing. Recent large language model
(LLM)-based quantum circuit generation has emerged as a promising automatic
solution. However, the fundamental challenges remain unaddressed: (i)
parameterized quantum gates require precise numerical values for optimal
performance, which also depend on multiple aspects, including the number of
quantum gates, their parameters, and the layout/depth of the circuits. (ii)
LLMs often generate low-quality or incorrect quantum circuits due to the lack
of quantum domain-specific knowledge. We propose QUASAR, an agentic
reinforcement learning (RL) framework for quantum circuits generation and
optimization based on tool-augmented LLMs. To align the LLM with
quantum-specific knowledge and improve the generated quantum circuits, QUASAR
designs (i) a quantum circuit verification approach with external quantum
simulators and (ii) a sophisticated hierarchical reward mechanism in RL
training. Extensive evaluation shows improvements in both syntax and semantic
performance of the generated quantum circuits. When augmenting a 4B LLM, QUASAR
has achieved the validity of 99.31% in Pass@1 and 100% in Pass@10,
outperforming industrial LLMs of GPT-4o, GPT-5 and DeepSeek-V3 and several
supervised-fine-tuning (SFT)-only and RL-only baselines.
It Takes Two: Your GRPO Is Secretly DPO
Authors: name: Yihong Wu, name: Liheng Ma, name: Lei Ding, name: Muzhi Li, name: Xinyu Wang, name: Kejia Chen, name: Zhan Su, name: Zhanguang Zhang, name: Chenyang Huang, name: Yingxue Zhang, name: Mark Coates, name: Jian-Yun Nie
Published: 2025-10-01 14:52:11

Group Relative Policy Optimization (GRPO) is a prominent reinforcement
learning algorithm for post-training Large Language Models (LLMs). It is
commonly believed that GRPO necessitates a large group size to ensure stable
training via precise statistical estimation, which incurs substantial
computational overhead. In this work, we challenge this assumption by reframing
GRPO as a form of contrastive learning, which reveals a fundamental connection
to Direct Preference Optimization (DPO). Motivated by DPO's empirical success,
we investigate the minimal two-rollout case (2-GRPO), a configuration
previously deemed infeasible. We provide a rigorous theoretical analysis to
validate 2-GRPO and demonstrate empirically that it achieves performance on par
with 16-GRPO, despite using only 1/8 of the rollouts and reducing training time
by over 70%.
BindWeave: Subject-Consistent Video Generation via Cross-Modal
  Integration
Authors: name: Zhaoyang Li, name: Dongjun Qian, name: Kai Su, name: Qishuai Diao, name: Xiangyang Xia, name: Chang Liu, name: Wenfei Yang, name: Tianzhu Zhang, name: Zehuan Yuan
Published: 2025-10-01 02:41:11

Diffusion Transformer has shown remarkable abilities in generating
high-fidelity videos, delivering visually coherent frames and rich details over
extended durations. However, existing video generation models still fall short
in subject-consistent video generation due to an inherent difficulty in parsing
prompts that specify complex spatial relationships, temporal logic, and
interactions among multiple subjects. To address this issue, we propose
BindWeave, a unified framework that handles a broad range of subject-to-video
scenarios from single-subject cases to complex multi-subject scenes with
heterogeneous entities. To bind complex prompt semantics to concrete visual
subjects, we introduce an MLLM-DiT framework in which a pretrained multimodal
large language model performs deep cross-modal reasoning to ground entities and
disentangle roles, attributes, and interactions, yielding subject-aware hidden
states that condition the diffusion transformer for high-fidelity
subject-consistent video generation. Experiments on the OpenS2V benchmark
demonstrate that our method achieves superior performance across subject
consistency, naturalness, and text relevance in generated videos, outperforming
existing open-source and commercial models.
PIPer: On-Device Environment Setup via Online Reinforcement Learning
Authors: name: Alexander Kovrigin, name: Aleksandra Eliseeva, name: Konstantin Grotov, name: Egor Bogomolov, name: Yaroslav Zharov
Published: 2025-09-29 20:03:05

Environment setup-the process of configuring the system to work with a
specific software project-represents a persistent challenge in Software
Engineering (SE). Automated environment setup methods could assist developers
by providing fully configured environments for arbitrary repositories without
manual effort. This also helps SE researchers to scale execution-based
benchmarks. However, recent studies reveal that even state-of-the-art Large
Language Models (LLMs) achieve limited success in automating this task. To
address this limitation, we tune a specialized model for environment setup. We
combine supervised fine-tuning for generating correct Bash scripts and
Reinforcement Learning with Verifiable Rewards (RLVR) to adapt it to the task
of environment setup. On EnvBench-Python, our method enables Qwen3-8B (a model
runnable on consumer hardware) to perform on par with larger models-Qwen3-32B
and GPT-4o. The training code and model checkpoints are available online:
https://github.com/JetBrains-Research/PIPer.
Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic
  Architectures
Authors: name: Marco Bronzini, name: Carlo Nicolini, name: Bruno Lepri, name: Jacopo Staiano, name: Andrea Passerini
Published: 2025-09-29 16:59:07

Despite their capabilities, Large Language Models (LLMs) remain opaque with
limited understanding of their internal representations. Current
interpretability methods, such as direct logit attribution (DLA) and sparse
autoencoders (SAEs), provide restricted insight due to limitations such as the
model's output vocabulary or unclear feature names. This work introduces
Hyperdimensional Probe, a novel paradigm for decoding information from the LLM
vector space. It combines ideas from symbolic representations and neural
probing to project the model's residual stream into interpretable concepts via
Vector Symbolic Architectures (VSAs). This probe combines the strengths of SAEs
and conventional probes while overcoming their key limitations. We validate our
decoding paradigm with controlled input-completion tasks, probing the model's
final state before next-token prediction on inputs spanning syntactic pattern
recognition, key-value associations, and abstract inference. We further assess
it in a question-answering setting, examining the state of the model both
before and after text generation. Our experiments show that our probe reliably
extracts meaningful concepts across varied LLMs, embedding sizes, and input
domains, also helping identify LLM failures. Our work advances information
decoding in LLM vector space, enabling extracting more informative,
interpretable, and structured features from neural representations.