Orionnnn/hub-stats huggingface dataset:
Artificial Hippocampus Networks for Efficient Long-Context Modeling
Authors: name: Yunhao Fang, name: Weihao Yu, name: Shu Zhong, name: Qinghao Ye, name: Xuehan Xiong, name: Lai Wei
Published: 2025-10-08 17:59:55

Long-sequence modeling faces a fundamental trade-off between the efficiency
of compressive fixed-size memory in RNN-like models and the fidelity of
lossless growing memory in attention-based Transformers. Inspired by the
Multi-Store Model in cognitive science, we introduce a memory framework of
artificial neural networks. Our method maintains a sliding window of the
Transformer's KV cache as lossless short-term memory, while a learnable module
termed Artificial Hippocampus Network (AHN) recurrently compresses
out-of-window information into a fixed-size compact long-term memory. To
validate this framework, we instantiate AHNs using modern RNN-like
architectures, including Mamba2, DeltaNet, and Gated DeltaNet. Extensive
experiments on long-context benchmarks LV-Eval and InfiniteBench demonstrate
that AHN-augmented models consistently outperform sliding window baselines and
achieve performance comparable or even superior to full-attention models, while
substantially reducing computational and memory requirements. For instance,
augmenting the Qwen2.5-3B-Instruct with AHNs reduces inference FLOPs by 40.5%
and memory cache by 74.0%, while improving its average score on LV-Eval (128k
sequence length) from 4.41 to 5.88. Code is available at:
https://github.com/ByteDance-Seed/AHN.
SHANKS: Simultaneous Hearing and Thinking for Spoken Language Models
Authors: name: Cheng-Han Chiang, name: Xiaofei Wang, name: Linjie Li, name: Chung-Ching Lin, name: Kevin Lin, name: Shujie Liu, name: Zhendong Wang, name: Zhengyuan Yang, name: Hung-yi Lee, name: Lijuan Wang
Published: 2025-10-08 11:48:59

Current large language models (LLMs) and spoken language models (SLMs) begin
thinking and taking actions only after the user has finished their turn. This
prevents the model from interacting during the user's turn and can lead to high
response latency while it waits to think. Consequently, thinking after
receiving the full input is not suitable for speech-to-speech interaction,
where real-time, low-latency exchange is important. We address this by noting
that humans naturally "think while listening." In this paper, we propose
SHANKS, a general inference framework that enables SLMs to generate unspoken
chain-of-thought reasoning while listening to the user input. SHANKS streams
the input speech in fixed-duration chunks and, as soon as a chunk is received,
generates unspoken reasoning based on all previous speech and reasoning, while
the user continues speaking. SHANKS uses this unspoken reasoning to decide
whether to interrupt the user and to make tool calls to complete the task. We
demonstrate that SHANKS enhances real-time user-SLM interaction in two
scenarios: (1) when the user is presenting a step-by-step solution to a math
problem, SHANKS can listen, reason, and interrupt when the user makes a
mistake, achieving 37.1% higher interruption accuracy than a baseline that
interrupts without thinking; and (2) in a tool-augmented dialogue, SHANKS can
complete 56.9% of the tool calls before the user finishes their turn. Overall,
SHANKS moves toward models that keep thinking throughout the conversation, not
only after a turn ends. Animated illustrations of Shanks can be found at
https://d223302.github.io/SHANKS/
SDQM: Synthetic Data Quality Metric for Object Detection Dataset
  Evaluation
Authors: name: Ayush Zenith, name: Arnold Zumbrun, name: Neel Raut, name: Jing Lin
Published: 2025-10-08 03:01:26

The performance of machine learning models depends heavily on training data.
The scarcity of large-scale, well-annotated datasets poses significant
challenges in creating robust models. To address this, synthetic data generated
through simulations and generative models has emerged as a promising solution,
enhancing dataset diversity and improving the performance, reliability, and
resilience of models. However, evaluating the quality of this generated data
requires an effective metric. This paper introduces the Synthetic Dataset
Quality Metric (SDQM) to assess data quality for object detection tasks without
requiring model training to converge. This metric enables more efficient
generation and selection of synthetic datasets, addressing a key challenge in
resource-constrained object detection tasks. In our experiments, SDQM
demonstrated a strong correlation with the mean Average Precision (mAP) scores
of YOLOv11, a leading object detection model, while previous metrics only
exhibited moderate or weak correlations. Additionally, it provides actionable
insights for improving dataset quality, minimizing the need for costly
iterative training. This scalable and efficient metric sets a new standard for
evaluating synthetic data. The code for SDQM is available at
https://github.com/ayushzenith/SDQM
BACHI: Boundary-Aware Symbolic Chord Recognition Through Masked
  Iterative Decoding on Pop and Classical Music
Authors: name: Mingyang Yao, name: Ke Chen, name: Shlomo Dubnov, name: Taylor Berg-Kirkpatrick
Published: 2025-10-08 00:02:56

Automatic chord recognition (ACR) via deep learning models has gradually
achieved promising recognition accuracy, yet two key challenges remain. First,
prior work has primarily focused on audio-domain ACR, while symbolic music
(e.g., score) ACR has received limited attention due to data scarcity. Second,
existing methods still overlook strategies that are aligned with human music
analytical practices. To address these challenges, we make two contributions:
(1) we introduce POP909-CL, an enhanced version of POP909 dataset with
tempo-aligned content and human-corrected labels of chords, beats, keys, and
time signatures; and (2) We propose BACHI, a symbolic chord recognition model
that decomposes the task into different decision steps, namely boundary
detection and iterative ranking of chord root, quality, and bass (inversion).
This mechanism mirrors the human ear-training practices. Experiments
demonstrate that BACHI achieves state-of-the-art chord recognition performance
on both classical and pop music benchmarks, with ablation studies validating
the effectiveness of each module.
DYMO-Hair: Generalizable Volumetric Dynamics Modeling for Robot Hair
  Manipulation
Authors: name: Chengyang Zhao, name: Uksang Yoo, name: Arkadeep Narayan Chaudhury, name: Giljoo Nam, name: Jonathan Francis, name: Jeffrey Ichnowski, name: Jean Oh
Published: 2025-10-07 17:53:56

Hair care is an essential daily activity, yet it remains inaccessible to
individuals with limited mobility and challenging for autonomous robot systems
due to the fine-grained physical structure and complex dynamics of hair. In
this work, we present DYMO-Hair, a model-based robot hair care system. We
introduce a novel dynamics learning paradigm that is suited for volumetric
quantities such as hair, relying on an action-conditioned latent state editing
mechanism, coupled with a compact 3D latent space of diverse hairstyles to
improve generalizability. This latent space is pre-trained at scale using a
novel hair physics simulator, enabling generalization across previously unseen
hairstyles. Using the dynamics model with a Model Predictive Path Integral
(MPPI) planner, DYMO-Hair is able to perform visual goal-conditioned hair
styling. Experiments in simulation demonstrate that DYMO-Hair's dynamics model
outperforms baselines on capturing local deformation for diverse, unseen
hairstyles. DYMO-Hair further outperforms baselines in closed-loop hair styling
tasks on unseen hairstyles, with an average of 22% lower final geometric error
and 42% higher success rate than the state-of-the-art system. Real-world
experiments exhibit zero-shot transferability of our system to wigs, achieving
consistent success on challenging unseen hairstyles where the state-of-the-art
system fails. Together, these results introduce a foundation for model-based
robot hair care, advancing toward more generalizable, flexible, and accessible
robot hair styling in unconstrained physical environments. More details are
available on our project page: https://chengyzhao.github.io/DYMOHair-web/.
MEMTRACK: Evaluating Long-Term Memory and State Tracking in
  Multi-Platform Dynamic Agent Environments
Authors: name: Darshan Deshpande, name: Varun Gangal, name: Hersh Mehta, name: Anand Kannappan, name: Rebecca Qian, name: Peng Wang
Published: 2025-10-01 18:34:03

Recent works on context and memory benchmarking have primarily focused on
conversational instances but the need for evaluating memory in dynamic
enterprise environments is crucial for its effective application. We introduce
MEMTRACK, a benchmark designed to evaluate long-term memory and state tracking
in multi-platform agent environments. MEMTRACK models realistic organizational
workflows by integrating asynchronous events across multiple communication and
productivity platforms such as Slack, Linear and Git. Each benchmark instance
provides a chronologically platform-interleaved timeline, with noisy,
conflicting, cross-referring information as well as potential
codebase/file-system comprehension and exploration. Consequently, our benchmark
tests memory capabilities such as acquistion, selection and conflict
resolution. We curate the MEMTRACK dataset through both manual expert driven
design and scalable agent based synthesis, generating ecologically valid
scenarios grounded in real world software development processes. We introduce
pertinent metrics for Correctness, Efficiency, and Redundancy that capture the
effectiveness of memory mechanisms beyond simple QA performance. Experiments
across SoTA LLMs and memory backends reveal challenges in utilizing memory
across long horizons, handling cross-platform dependencies, and resolving
contradictions. Notably, the best performing GPT-5 model only achieves a 60\%
Correctness score on MEMTRACK. This work provides an extensible framework for
advancing evaluation research for memory-augmented agents, beyond existing
focus on conversational setups, and sets the stage for multi-agent,
multi-platform memory benchmarking in complex organizational settings
Scalable In-context Ranking with Generative Models
Authors: name: Nilesh Gupta, name: Chong You, name: Srinadh Bhojanapalli, name: Sanjiv Kumar, name: Inderjit Dhillon, name: Felix Yu
Published: 2025-10-06 21:41:58

In-context Ranking (ICR) is an emerging paradigm for Information Retrieval
(IR), which leverages contextual understanding of LLMs by directly
incorporating the task description, candidate documents, and the query into the
model's input prompt and tasking the LLM to identify relevant document(s).
While it is effective, efficiency is a significant challenge in this paradigm,
especially as the candidate list grows due to quadratic/super-linear scaling of
attention operation with context length. To this end, this paper first
identifies inherent and exploitable structures in the attention of LLMs
finetuned for ICR: (1) inter-document block sparsity: attention is dense within
each document block but sparse across different documents in the context; and
(2) query-document block relevance: the attention scores from certain query
tokens to a document block in middle layers strongly correlate with that
document's actual relevance. Motivated by these observations, we introduce
BlockRank (Blockwise In-context Ranking), a novel method that adapts the
attention operation in an LLM by (a) architecturally enforcing the observed
inter-document block sparsity, reducing attention complexity from quadratic to
linear without loss in performance, and (b) optimizing query-document block
relevance for true relevant documents during fine-tuning using an auxiliary
contrastive training objective, improving retrieval in attention. Experiments
on BEIR, MSMarco and NQ with Mistral-7B demonstrate that FLARE Mistral matches
or outperforms existing SOTA listwise rankers and controlled fine-tuned
baseline while being significantly more efficient at inference (4.7x for 100
MSMarco documents in context) and scaling gracefully to long-context
shortlists, around 500 documents in-context (approximately 100K context length)
within a second, presenting a scalable and effective solution for ICR.
Let it Calm: Exploratory Annealed Decoding for Verifiable Reinforcement
  Learning
Authors: name: Chenghao Yang, name: Lin Gui, name: Chenxiao Yang, name: Victor Veitch, name: Lizhu Zhang, name: Zhuokai Zhao
Published: 2025-10-06 18:15:43

Reinforcement learning with verifiable rewards (RLVR) is a powerful paradigm
for enhancing the reasoning capabilities of large language models (LLMs), yet
its success hinges on effective exploration. An ideal exploration strategy must
navigate two fundamental challenges: it must preserve sample quality while also
ensuring training stability. While standard fixed-temperature sampling is
simple, it struggles to balance these competing demands, as high temperatures
degrade sample quality and low temperatures limit discovery. In this work, we
propose a simpler and more effective strategy, Exploratory Annealed Decoding
(EAD), grounded in the insight that exploration is most impactful on early
tokens which define a sequence's semantic direction. EAD implements an
intuitive **explore-at-the-beginning, exploit-at-the-end** strategy by
annealing the sampling temperature from high to low during generation. This
dynamic schedule encourages meaningful, high-level diversity at the start, then
gradually lowers the temperature to preserve sample quality and keep the
sampling distribution close to the target policy, which is essential for stable
training. We demonstrate that EAD is a lightweight, plug-and-play method that
significantly improves sample efficiency, consistently outperforming
fixed-temperature sampling across various RLVR algorithms and model sizes. Our
work suggests that aligning exploration with the natural dynamics of sequential
generation offers a robust path to improving LLM reasoning.
ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in
  Complex Chart Question Answering
Authors: name: Rachneet Kaur, name: Nishan Srishankar, name: Zhen Zeng, name: Sumitra Ganesh, name: Manuela Veloso
Published: 2025-10-06 06:05:36

Recent multimodal LLMs have shown promise in chart-based visual question
answering, but their performance declines sharply on unannotated charts, those
requiring precise visual interpretation rather than relying on textual
shortcuts. To address this, we introduce ChartAgent, a novel agentic framework
that explicitly performs visual reasoning directly within the chart's spatial
domain. Unlike textual chain-of-thought reasoning, ChartAgent iteratively
decomposes queries into visual subtasks and actively manipulates and interacts
with chart images through specialized actions such as drawing annotations,
cropping regions (e.g., segmenting pie slices, isolating bars), and localizing
axes, using a library of chart-specific vision tools to fulfill each subtask.
This iterative reasoning process closely mirrors human cognitive strategies for
chart comprehension. ChartAgent achieves state-of-the-art accuracy on the
ChartBench and ChartX benchmarks, surpassing prior methods by up to 16.07%
absolute gain overall and 17.31% on unannotated, numerically intensive queries.
Furthermore, our analyses show that ChartAgent is (a) effective across diverse
chart types, (b) achieve the highest scores across varying visual and reasoning
complexity levels, and (c) serves as a plug-and-play framework that boosts
performance across diverse underlying LLMs. Our work is among the first to
demonstrate visually grounded reasoning for chart understanding using
tool-augmented multimodal agents.
GRACE: Generative Representation Learning via Contrastive Policy
  Optimization
Authors: name: Jiashuo Sun, name: Shixuan Liu, name: Zhaochen Su, name: Xianrui Zhong, name: Pengcheng Jiang, name: Bowen Jin, name: Peiran Li, name: Weijia Shi, name: Jiawei Han
Published: 2025-10-06 05:46:56

Prevailing methods for training Large Language Models (LLMs) as text encoders
rely on contrastive losses that treat the model as a black box function,
discarding its generative and reasoning capabilities in favor of static
embeddings. We introduce GRACE (Generative Representation Learning via
Contrastive Policy Optimization), a novel framework that reimagines contrastive
signals not as losses to be minimized, but as rewards that guide a generative
policy. In GRACE, the LLM acts as a policy that produces explicit,
human-interpretable rationales--structured natural language explanations of its
semantic understanding. These rationales are then encoded into high-quality
embeddings via mean pooling. Using policy gradient optimization, we train the
model with a multi-component reward function that maximizes similarity between
query positive pairs and minimizes similarity with negatives. This transforms
the LLM from an opaque encoder into an interpretable agent whose reasoning
process is transparent and inspectable. On MTEB benchmark, GRACE yields broad
cross category gains: averaged over four backbones, the supervised setting
improves overall score by 11.5% over base models, and the unsupervised variant
adds 6.9%, while preserving general capabilities. This work treats contrastive
objectives as rewards over rationales, unifying representation learning with
generation to produce stronger embeddings and transparent rationales. The
model, data and code are available at https://github.com/GasolSun36/GRACE.
Adaptive Pruning for Increased Robustness and Reduced Computational
  Overhead in Gaussian Process Accelerated Saddle Point Searches
Authors: name: Rohit Goswami, name: Hannes Jónsson
Published: 2025-10-07 15:27:39

Gaussian process (GP) regression provides a strategy for accelerating saddle
point searches on high-dimensional energy surfaces by reducing the number of
times the energy and its derivatives with respect to atomic coordinates need to
be evaluated. The computational overhead in the hyperparameter optimization
can, however, be large and make the approach inefficient. Failures can also
occur if the search ventures too far into regions that are not represented well
enough by the GP model. Here, these challenges are resolved by using
geometry-aware optimal transport measures and an active pruning strategy using
a summation over Wasserstein-1 distances for each atom-type in farthest-point
sampling, selecting a fixed-size subset of geometrically diverse configurations
to avoid rapidly increasing cost of GP updates as more observations are made.
Stability is enhanced by permutation-invariant metric that provides a reliable
trust radius for early-stopping and a logarithmic barrier penalty for the
growth of the signal variance. These physically motivated algorithmic changes
prove their efficacy by reducing to less than a half the mean computational
time on a set of 238 challenging configurations from a previously published
data set of chemical reactions. With these improvements, the GP approach is
established as, a robust and scalable algorithm for accelerating saddle point
searches when the evaluation of the energy and atomic forces requires
significant computational effort.
Scientific Algorithm Discovery by Augmenting AlphaEvolve with Deep
  Research
Authors: name: Gang Liu, name: Yihan Zhu, name: Jie Chen, name: Meng Jiang
Published: 2025-10-07 15:49:51

Large language models hold promise as scientific assistants, yet existing
agents either rely solely on algorithm evolution or on deep research in
isolation, both of which face critical limitations. Pure algorithm evolution,
as in AlphaEvolve, depends only on the internal knowledge of LLMs and quickly
plateaus in complex domains, while pure deep research proposes ideas without
validation, resulting in unrealistic or unimplementable solutions. We present
DeepEvolve, an agent that integrates deep research with algorithm evolution,
uniting external knowledge retrieval, cross-file code editing, and systematic
debugging under a feedback-driven iterative loop. Each iteration not only
proposes new hypotheses but also refines, implements, and tests them, avoiding
both shallow improvements and unproductive over-refinements. Across nine
benchmarks in chemistry, mathematics, biology, materials, and patents,
DeepEvolve consistently improves the initial algorithm, producing executable
new algorithms with sustained gains. By bridging the gap between unguided
evolution and research without grounding, DeepEvolve provides a reliable
framework for advancing scientific algorithm discovery. Our code is available
at https://github.com/liugangcode/deepevolve.
On Code-Induced Reasoning in LLMs
Authors: name: Abdul Waheed, name: Zhen Wu, name: Carolyn Rosé, name: Daphne Ippolito
Published: 2025-09-25 19:57:36

Code data has been shown to enhance the reasoning capabilities of large
language models (LLMs), but it remains unclear which aspects of code are most
responsible. We investigate this question with a systematic, data-centric
framework. We construct parallel instruction datasets in ten programming
languages and apply controlled perturbations that selectively disrupt
structural or semantic properties of code. We then finetune LLMs from five
model families and eight scales on each variant and evaluate their performance
on natural language, math, and code tasks. Across 3,331 experiments, our
results show that LLMs are more vulnerable to structural perturbations than
semantic ones, particularly on math and code tasks. Appropriate abstractions
like pseudocode and flowcharts can be as effective as code, while encoding the
same information with fewer tokens without adhering to original syntax can
often retain or even improve performance. Remarkably, even corrupted code with
misleading signals remains competitive when surface-level regularities persist.
Finally, syntactic styles also shape task-specific gains with Python favoring
natural language reasoning and lower-level languages such as Java and Rust
favoring math. Through our systematic framework, we aim to provide insight into
how different properties of code influence reasoning and inform the design of
training data for enhancing LLM reasoning capabilities.
The Valley of Code Reasoning: Scaling Knowledge Distillation of Large
  Language Models
Authors: name: Muyu He, name: Muhammad Ali Shafique, name: Anand Kumar, name: Tsach Mackey, name: Nazneen Rajani
Published: 2025-10-07 16:32:09

Distilling the thinking traces of a Large Language Model (LLM) with reasoning
capabilities into a smaller model has been proven effective. Yet, there is a
scarcity of work done on how model performances scale with the quantity of
distillation data. In this work, we study the scaling trend of distilling
competitive coding skills on two small non-reasoning LLMs. We validate the
hypothesis that there is a valley of code reasoning: downstream
performance on competitive coding first drops as data quantity increases, then
it steadily increases in a sharper-than-log-linear fashion. Having identified
the trend, we further fine-tune the models at two different distillation stages
on the same data to ground conclusions on their respective learning phases. We
learn that across stages in the low and medium-low data regimes, small models
benefit significantly from easier coding questions than from harder ones. We
also find that, surprisingly, the correctness of outputs in training data makes
no difference to distillation outcomes. Our work represents a step forward in
understanding the training dynamics of code reasoning distillation outside
intuition