Sound
Loud and Clear: Dynamic Activation Steering for Improving Speech Intelligibility in Noisy Environments
Seymanur Akti and Alexander Waibel
Speech becomes less intelligible in noisy environments, and humans naturally adapt their voice to compensate. Inspired by this behavior, we investigate whether a text-to-
speech (TTS) model can be guided to produce more intelligible
speech using activation steering, without retraining. We focus on two characteristics of the Lombard effect: increased vocal effort and hyper-articulation. We introduce a
prompt-relative steering mechanism that prevents steering effects from accumulating during generation while allowing their strength to be adjusted dynamically. Across seen and unseen speakers and multiple
languages, our method produces systematic changes in Lombard-related acoustic features, preserves speaker similarity (89-95%), and reduces WER under background noise by 7-22% at 1 dB SNR. These results show that pretrained TTS models can be dynamically controlled to generate more intelligible
speech without retraining.
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Artificial Intelligence
Matching Object or Relation? Tracing Abstract Reasoning Inside VLMs
Gouki Minegishi, Hiroki Furuta, Takeshi Kojima et al. (+2 more)
Vision Language Models (VLMs) excel on visual
benchmarks but fail systematically on tasks requiring abstract
reasoning. Existing
benchmarks document this failure but cannot say why it happens or which cognitive capability is missing. We close this gap by adopting the Relational Match-to-Sample (RMTS) paradigm from comparative and developmental psychology and pairing it with a mechanistic analysis of the model's internals. On a parametrically controlled stimulus set evaluated across frontier API models (GPT, Claude, Gemini) and three open-source families (Qwen3.5, Gemma-4, InternVL3), we identify four levers that shift VLMs toward the relational match—capability tier, model scale, the number of objects per scene, and the absence of per-object stimulus noise—together producing a developmental-like trajectory that mirrors the human relational shift. Opening up the model, a per-layer representational similarity analysis and a causal mediation analysis reveal that VLM abstract
reasoning is implemented by two competing
circuits: an early
circuit that organises images by their surface object features, and a late
circuit that organises them by their abstract relation. Extending the analysis to ARC-AGI-1, we find that ablating the relational heads identified on RMTS degrades performance more than ablating random heads, indicating that the relational
circuit is recruited beyond our controlled stimuli. We hope this mechanism-level view serves as a step toward understanding how abstract
reasoning is implemented in VLMs.
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All authors Gouki Minegishi, Hiroki Furuta, Takeshi Kojima, Yusuke Iwasawa, Yutaka Matsuo
Cryptography and Security
SkillPoison: Progressive Skill Poisoning via Successful Experiences
Lizhi Zhang, Xin He, Dianxuan Fu et al. (+5 more)
Self-improving LLM
agents increasingly distill successful experiences into persistent, reusable skills. Existing skill attack methods corrupt this learning pipeline by injecting malicious triggers, behaviors, or false facts into individual experiences or extracted skills. However, such attacks are easily detected, and the injected malicious behaviors often fail to accumulate as persistent skills. In this paper, we show that skill poisoning can arise even from verified successful experiences, without making any individual trajectory malicious. Based on this insight, we propose SkillPoison, a novel framework that progressively poisons skill via successful experiences. SkillPoison first constructs a set of successful experiences that reinforce a target behavior, and then removes the contextual conditions that constrain when the behavior applies. Rather than injecting malicious content, SkillPoison shapes how the skill extractor generalizes, allowing useful behavior to support task success while inducing harmful behavior when they are misapplied. Extensive experiments on three
benchmarks show that SkillPoison achieves 95.71% attack success rates, while all injected experiences remain task-correct and pass verification and lexical inspection. Our code, data and implementation details are available for the community at https://github.com/DEEP-JLU/SkillPoison.
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All authors Lizhi Zhang, Xin He, Dianxuan Fu, Yuyuan Feng, Jiatong Li, Qi Wang, Xin Wang, Qinggang Zhang
Networking
From ASIC to Fleet: Lessons from Building and Operating a Hyperscaler NIC
Prankur Gupta, Alexander Duyck, Jakub Kicinski et al. (+25 more)
We describe the operational infrastructure built to deploy and operate fbnic, a custom multi-host NIC, across hundreds of thousands of production hosts at Meta. Vendor multi-host NICs, designed by retrofitting single-host architectures, suffered from shared firmware and buffers that created cascading isolation failures over seven years. fbnic eliminates these through physical isolation, but shifting to in-house hardware shifts the entire operational burden to the hyperscaler. We present a hardware-in-the-loop CI pipeline testing firmware, driver, and kernel cross-products; a unified observability pipeline co-locating NIC and switch counters for cross-layer fault attribution; a driver-first architecture with fewer than ten firmware message types; a targeted firmware upgrade orchestrator at sub-sled granularity; and scoped repair automation confining blast radius to individual host slices. Over ten months, fbnic achieved a 12X reduction in unplanned unavailability, 37% lower mean time to repair, and 2.3X fewer hardware swaps compared to vendor NICs on the same platform.
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All authors Prankur Gupta, Alexander Duyck, Jakub Kicinski, Joseph Provine, Neal Peacock, Prabhakaran Ganesan, Rajiv Krishnamurthy, Chen Liu, Akshay Viswakumar, Timothy Vitkin, Jie Meng, Beatriz Padilla Hernandez, Michael Edwards, Andrei Kozlov, Viren Nathan, Tianyi Cui, Joy Chaoyue Xiong, Raul Hormazabal, Mohsin Bashir, Fred Feng, Nathan Walker, Lavin Khandelwal, Matt Maia, Milo Piazza, Mohanraj Thillainayagam, Mahmoud Mehr, Amithash Prasad, Lee Trager
Computation and Language
Monte Carlo Estimation for KV Cache Eviction
Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer et al. (+5 more)
Most KV-cache eviction methods ask, in effect, which
memory appeared important while reading the
prompt? We instead ask which
memory will matter while answering? Since decoding queries are unavailable at eviction time, prior future-aware methods rely on pseudo-responses or synthetic future-query estimates. We cast fixed-budget future-aware eviction as distributional estimation over plausible model-conditional query trajectories and introduce LORE-KV (Lookahead Output-perturbation with Reliability-weighted Ensembles for Key-Value caches), a training-free method that samples short autoregressive continuations from the frozen target model and uses their response-side query states to estimate
prompt-
token utility.
Tokens are scored by projected leave-one-out
attention-output deletion cost and aggregated across sampled futures with optional trajectory weighting. The temporary continuations are discarded before final decoding, requiring no auxiliary model or training. Ablations isolate the mechanism: at B=128, a single response-side continuation recovers about 89% of the gain over the
prompt-window control, while additional futures provide smaller improvements. At B=128, LORE-KV raises the LongBench average on Qwen2.5-14B from 45.49 to 48.24 (+2.75) and the 16K RULER average on Mistral-7B from 45.20 to 51.05 (+5.85); on Llama-3.1-8B, the RULER margin over AnDPro widens to +8.13 and +11.65 points at 64K and 128K contexts. Gains diminish at larger cache budgets and coexist with task-level regressions. LORE-KV incurs 1.46-2.77x AnDPro's per-sample wall-clock time as a one-time compression overhead across six dense and hybrid-
attention backbones. Project webpage: https://ahsanbilal7.github.io/train_free_kv/
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All authors Ahsan Bilal, Muhammad Ahmed Mohsin, Muhammad Umer, Wajih Hassan Raza, Atta Ul Asad, Young D. Kwon, Michal Valko, Dean F. Hougen
Information Theory
Cold-Atom Rydberg Array with Spatiotemporal Multiplexing for High-Sensitivity Wireless Communications
Jian Xiao, Tierui Gong and Chau Yuen
An effective Cold-Atom Rydberg Array (CoRA) architecture is investigated, which addresses the fundamental thermal noise limit in classical electromagnetics and intrinsic Doppler broadening as well as
photon shot noise in conventional hot-vapor
cell-based atomic receivers. However, translating the CoRA-based
quantum electrometers into practical wireless communication receivers is bottlenecked by both the macroscopic readout dead-times and nonlinear binomial population counts rather than the additive-Gaussian baseband samples in conventional receivers. To address these critical challenges and facilitate the practicality of the CoRA, in this paper, a subarray scheduling-based spatiotemporal multiplexing (STM) framework is first proposed for gap-free symbol interrogation within an admitted packet. Additionally, a signal detection scheme based on a joint binomial particle smoother (JBPS) is proposed for burst-wise state and data inference. Furthermore, the weak-signal
quantum-projection-noise (QPN) limit and the state-averaged finite-alphabet rate are derived for CoRA-assisted wireless communications. Numerical results demonstrate that: 1) The proposed STM-based CoRA scheme resolves the macroscopic temporal bottleneck for continuous signal sampling; 2) The proposed JBPS approach effectively mitigates the nonlinear noise amplification to achieve a lower bit error rate; and 3) Performance analysis reveals a fundamental sensitivity-sustainability tradeoff that increasing the number of atoms effectively suppresses the QPN, but simultaneously reduces the sustainable packet repetition rate.
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Sound
Hiding Tool Latency in On-Device Cascaded Voice Agent through Speculative Execution
Kyudan Jung, Hyunsin Park, Yoonhyung Lee et al. (+5 more)
Tool-augmented
speech assistants typically serialize automatic
speech recognition, large
language model inference, and external tool execution. As a result, tool latency is incurred only after the user has finished speaking and the LLM has identified the required tool calls. We present speculative tool execution for on-device cascaded voice
agents, which predicts tool requests from partial ASR hypotheses and initiates tool execution while
speech is still being received, thereby reducing end-to-end response latency. Our approach introduces a Predictor module that anticipates tool calls during
speech recognition, executes them speculatively, and caches the results. The cached outputs are then injected into the LLM
prompt, enabling faster responses. Additionally, to mitigate errors caused by user self-corrections during
speech, we employ a rule-based validation mechanism that selectively injects only valid cached results. As a final safeguard, the LLM retains the ability to issue tool calls directly, ensuring that the latency of our framework is upper-bounded by the baseline serial execution pipeline in the worst case. We evaluate our method using live measurements from a fully implemented Android voice assistant. Our approach reduces the median time-to-first-audio from 5.79,s to 4.60,s and decreases the standard deviation from 3.49,s to 2.81,s, resulting in more predictable response latency.
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All authors Kyudan Jung, Hyunsin Park, Yoonhyung Lee, Jinhwan Park, Jinhyeok Yang, KiHyun Nam, Jaegul Choo, Jinkyu Lee
Artificial Intelligence
Towards the Automatic Synthesis of Interpretable Chess Tactics
Abhijeet Krishnan and Chris Martens
State-of-the-art reinforcement learning
agents are capable of outperforming human experts at games like chess, Go and StarCraft II. These
agents do not simply take advantage of their digital hardware in being able to react and calculate faster than humans, but employ better strategies that lead to more victories. Interpreting these strategies would give human players valuable insight into how to improve their play. In this preliminary work, we propose a symbolic sub-policy model for playing chess. Inspired by chess tactics, our model attempts to incorporate domain knowledge to improve interpretability. We adapt patterns learned by an inductive logic programming system called PAL to derive our model. We contribute a divergence metric to evaluate our model against a random baseline, and find a set of tactics that is able to suggest moves of similar playing strength to a human beginner. Finally, we propose a computational evaluation scheme for the model by augmenting an off-the-shelf engine with it.
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Cryptography and Security
HarnessSecurity-Bench: Do Security Mechanisms Really Protect Coding Agent Harnesses?
Zhengyang Zhu, Liming Huang, Runmin Ji et al. (+8 more)
Coding
agent harnesses mediate tool use and authorize actions, yet their security mechanisms and runtime effects remain incompletely characterized. We present HarnessSecurity, the first systematic empirical study and
benchmark of open- and closed-source coding
agent harnesses. First, we derive a ten-mechanism taxonomy and then assess 400 harness-mechanism
cells using independent ratings by researchers and large
language model (LLM) judges. We find that about half of confirmed mechanism implementations are opt-in, while closed-source harnesses exhibit substantial evidence gaps. Second, we introduce HarnessSecurity-Bench, a
benchmark of 23 tasks across five attack surfaces without sacrificing legitimate task requirements. Using separate deterministic oracles to measure task utility and attack effects with security setting comparisons, we evaluate nine mechanisms across six leading harnesses: Claude Code, Codex CLI, Gemini CLI, gptme, Qwen Code, and GitHub Copilot. Under a controlled LLM baseline GLM-5.2, we conduct 2,500 trials, recording 81,155 tool calls and over 2.2 billion
tokens. Enabling auto-approve increases utility and raises attack success from 29.2% to 95.6%. Network isolation and read-only mode reduce attack effects with substantial utility losses, while command allowlisting and command denylisting reduce attack effects with a small utility loss and a utility gain, respectively. Task-level cases show that restrictions on a shared capability can obstruct both legitimate and malicious operations, and that allowed tools or commands can leave unauthorized operations reachable through alternative execution paths. Harness providers should make security settings verifiable, test alternative execution paths to protected operations, and assess attack effects alongside task utility and execution costs.
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All authors Zhengyang Zhu, Liming Huang, Runmin Ji, Mingxi Ye, Zihan Zhou, Hanyang Guo, Jingwen Wu, Yuhan Ye, Yuming Feng, Hong-Ning Dai, Zibin Zheng
Artificial Intelligence
Learning Explainable Representations of Complex Game-playing Strategies
Abhijeet Krishnan, Colin M. Potts, Arnav Jhala et al. (+3 more)
As part of learning to play complex games, human players develop develop abstractions for concepts and strategies of gameplay consistent with game rules to improve their performance. These concepts are applied to explain other players' actions, and to inform their own actions in-game. Understanding other players' strategies is a crucial part of such improvement, but requires time and effort. In this paper, we propose a strategy similar to human cognition for training RL
agents to synthesize learned strategies and policies as executable procedures based on sequences of gameplay actions. We present methods to automatically learn such programs to play chess and to solve tasks in a grid-based environment. We show that the learned strategies produce effective actions, and can be learned from gameplay data.
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All authors Abhijeet Krishnan, Colin M. Potts, Arnav Jhala, Harshad Khadilkar, Shirish Karande, Chris Martens
Disordered Systems and Neural Networks
Asymptotic Analysis of Empirical Risk Minimization on Entry-wise i.i.d. Heavy-Tailed Data
Kaito Takanami, Takashi Takahashi and Yoshiyuki Kabashima
Many real-world
datasets exhibit unusually large values far more frequently than predicted by Gaussian models. Heavy-tailed distributions capture this behavior, yet evaluating learning performance under them remains challenging because rare, large feature entries retain non-vanishing effects even in high dimensions. Even in the canonical setting of empirical risk minimization for linear regression with entry-wise i.i.d. symmetric $\alpha$-stable data, a precise asymptotic characterization of prediction has been lacking. In this work, we introduce a functional order parameter that describes the random effective problem associated with each coefficient. Using the replica method, we fully characterize the generalization error in the proportional high-dimensional limit where the sample size and feature dimension diverge at a fixed ratio. Additionally, this analysis establishes a heavy-tail universality law, scaling laws relating typical errors to prediction reliability, and the Bayes-optimal prediction error. In addition to characterizing the effects of extreme entries on the learning process, our method applies broadly to other systems with persistent local heterogeneity.
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Earth and Planetary Astrophysics
Hubble Space Telescope Observations of Active Asteroid (101955) Bennu
David jewitt, Max Mutchler and Yoonyoung Kim
Measurements from NASA's OSIRIS-REx spacecraft revealed (101955) Bennu as a member of the active asteroid class. These objects exhibit transient, comet-like activity, commonly producing particle trails best observed from a vantage point close to the projected orbit plane. Accordingly, we obtained deep, high resolution observations of Bennu at orbit plane crossing in 2023, using the Hubble Space
Telescope. We find no evidence for ejected material, and place model-dependent limits to the mass loss of dM/dt < 0.12 kg/s from an analysis of the near-
nucleus surface brightness profiles and dM/dt < 6e-3 kg/s from the absence of a measurable particle trail. These limits are one to two orders of magnitude smaller than rates of mass loss measured remotely in other active asteroids and raise the possibility that low activity in many or most asteroids might fall beneath the prevailing thresholds offered by remote observations. Taking a still smaller mass loss rate estimated from OSIRIS-REx data, we calculate a specific rate L = 1e-7 kg/km2/s and extrapolate to the entire main-belt asteroid population. We estimate a cumulative
dust production rate 850/r_min**1/2 [kg/s], where r_min is the minimum asteroid radius in meters. For r_min > 1 m, this is <1% to 10% of the rate needed to sustain the Zodiacal cloud in steady state.
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Cryptography and Security
CISB-Bench: An Auditable Source–IR Dataset of Compiler-Introduced Security Bugs
Saatvik Pradhan, Anuroop Saini, Pranav Attrey et al. (+1 more)
Compiler-introduced security bugs (CISBs) arise when an optimization, lowering, or instrumentation decision changes a security-relevant property of the generated program. They are difficult to study because their evidence is distributed across issue reports, reduced tests, historical configurations, and compiler artifacts; a security-related report also does not imply that every associated reduction establishes a security-bearing compiler failure. We present CISB-Bench, an auditable
dataset of 429 exact C-program rows mined from GCC and LLVM. Each row contains its C reduction, a standardized LLVM IR analysis bundle at -O0 through -O3, public provenance, a final binary label, and a primary mechanism or boundary annotation. Two reviewers independently labeled the fixed corpus, agreeing on 369 rows (86.0%, Cohen's kappa=0.662); the 60 disagreements were adjudicated. The final
dataset comprises 280 CISBs and 149 hard non-CISB cases. The prediction task is to recover this reviewed exact-row label from the supplied artifacts; it is not a claim that standardized IR alone reproduces every historical compiler failure. We characterize the security mechanisms and evidence boundaries represented by the corpus, and demonstrate how its paired artifacts support source-only, IR-aware, and joint analyses. CISB-Bench provides a reusable, inspectable target for compiler-security mining and detection research.
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All authors Saatvik Pradhan, Anuroop Saini, Pranav Attrey, Suman Saha
Artificial Intelligence
Measuring climate backlash in Twitter and Reddit archives: Lexical definitions, recorded responses and participant turnover
Wentao Xu
Social media archives are often used to study resistance to
climate action, but words, response counters and observed participants do not measure the same social process. We examine four supplied Twitter and Reddit archives by processing all registered files without sampling and applying transparent, non-exclusive lexical rules. The study links frame co-occurrence to source-specific temporal and response models, then separates event-period changes among returning authors from participant turnover. Renewable-energy terms accompany cost-related
language on Reddit, yet narrower backlash phrases sharply reduce cross-source contrasts and reverse the sign of the Paris Agreement contrast in submissions. Cross-discourse history does not improve eligible primary-context forecasts. Denial/hoax terms are associated with higher recorded Twitter likes, whereas Reddit response associations depend on frame, outcome and author specification. Around the 2019 global
climate strike, returning-author expression and participant turnover both contribute to increased protest-
language shares. An archive endpoint prevents the corresponding
Climate Twitter migration inference. Most crossed-cluster estimates lack released intervals, and joint author/month response covariance estimates fail, restricting formal inference. These results show how operational definitions, platform-specific response fields and observation boundaries shape what can be claimed about
climate backlash. The contribution is an archive-based account of these measurement consequences, rather than a measure of individual opposition, persuasion or advocacy-induced backlash.
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Optics
Direct visualization of non-equilibrium heat extraction by a metasurface absorber
Shu Watanabe, Robert E. Simpson, Takuo Tanaka et al. (+1 more)
Metasurface heat absorption is conventionally characterized by far-field infra-red spectroscopy under
equilibrium conditions. The approach provides information on the surface emissivity but does not directly reveal heat transferred by electromagnetic absorption. To understand the resulting temperature change, however, the associated heat transfer must be considered. Here, we directly observe heat extracted by electromagnetic absorption using infrared thermography. By monitoring the cooling dynamics of a copper plate placed in contact with a metasurface-integrated thermoelectric device, we show that the copper plate cools faster than one placed on an unstructured planar surface (control). This result provides direct evidence of heat transfer from the adjacent solid to the metasurface; revealing a metasurface-mediated electromagnetic absorption heat transfer mechanism. These findings provide new insight into nonequilibrium thermal processes enabled by metasurface absorbers.
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All authors Shu Watanabe, Robert E. Simpson, Takuo Tanaka, Wakana Kubo
Differential Geometry
The Sharp Isoperimetric Inequality in the First Heisenberg Group
Wei-Chun Chen, Wei-Ting Kao and Chun-Kai Lien
We prove the sharp horizontal isoperimetric inequality in the first Heisenberg group for all sets of finite horizontal perimeter and positive finite volume, with equality precisely for left translates and dilates of the Pansu ball.
Algebraic Topology
A Finite-Geometric Obstruction and a Polytopal Separation of Buchstaber Invariants
Suyoung Choi and Hyeontae Jang
We construct a polytopal simplicial sphere that admits a mod 2 characteristic map but no mod 3 characteristic map, and hence no integral one. This shows that the Buchstaber number of a polytopal sphere can be strictly smaller than its real Buchstaber number, and gives a negative answer to the toric lifting problem.
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Statistical Methodology
Measuring Negotiation Traits with Collaborative Choice Data: A Two-Stage Latent Variable Model
Tongxin Zhang, Yunxiao Chen, Jiangang Hao et al. (+4 more)
Consider a collaborative decision-making setting in which team members with different reward structures need to make a joint decision. Repeated collaborative decisions can reflect individual differences in team members' negotiation ability. However, extracting such information is challenging because each observed response arises from a collaborative process involving multiple participants and their reward structures. In this paper, we propose a two-stage probabilistic measurement model for structured collaborative choice tasks. In the first stage, known member-specific rewards and latent participant negotiation traits jointly determine team-option utilities, from which a latent team choice is generated. In the second stage, individual responses are modeled conditional on the latent team choice, with possible deviations that depend on participants' own rewards. Participant-level
covariates can also be incorporated through a structural model. A simulation study shows satisfactory recovery of the participant traits and model parameters across different sample sizes and item sizes. An application to data from simulated collaborative negotiation tasks shows choice prediction accuracy well above the random-choice baseline and meaningful associations between the estimated traits and external criterion variables. The proposed framework provides a psychometric approach to measuring participant-level negotiation traits with collaborative choice data.
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All authors Tongxin Zhang, Yunxiao Chen, Jiangang Hao, Emily Kerzabi, Patrick Kyllonen, Irini Moustaki, Tao Xin
Robotics
Beyond Task Reward: A Controller-Restriction Protocol for Evaluating Embodiment-Dependent Competence
Siyuan Zhang
Co-design methods optimize a
robot's body and controller jointly and judge the result by one number, the task reward of the fully optimized pair. That number cannot separate morphologies whose competence depends on the controller to very different degrees. We evaluate a morphology by restricting its controller instead, recording the task competence it retains under an explicitly declared, low-complexity controller family, environment, task and search budget. On three EvoGym locomotion tasks, task reward explains only $39\%$, $33\%$ and $10\%$ of the variance in this quantity, and
geometric descriptors do not predict it under run-grouped cross-validation. The measurement is reliable across optimizer restarts (ICC$(2,k) = 0.956$–$0.986$) but depends on the declared family: phasing the drive by actuator index instead of position ranks the same morphologies at Spearman $0.50$–$0.63$ and reverses reward-matched pairs. As a second search objective the axis improved competence at matched task reward in $3$ of $5$ paired runs, short of a pre-registered bar of $4$. Used after an ordinary reward-only search instead, to choose within its top task-reward band, it selected a different body in all $8$ runs offering a choice, at a cost of at most $0.10$ reward units, and in $6$ of $8$ that body also scored higher under a held-out family. Restricted-control competence is therefore a reportable property of a co-designed morphology, interpretable only with the controller family that defines it.
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Machine Learning
Complementary Supervised and Self-Supervised Representations for Out-of-Distribution Graph Learning
Qingying Hao, Zikang Chen, Chuxuan Hu et al. (+4 more)
Out-of-distribution (OOD) generalization remains challenging for graph neural networks (GNNs), as graph distributions can vary substantially across time and domains. Supervised and self-supervised graph representation learning are guided by distinct objectives and offer different perspectives on graph representations. In this work, we study whether self-supervised representations (SSL) can provide complementary signals to improve supervised OOD node classification. We develop two backbone-agnostic frameworks that exploit such information at different stages of learning and prediction. Co-Train jointly learns supervised and SSL representations and adaptively integrates them during training, while Dual-Space
Retrieval performs non-parametric prediction in the two representation spaces and combines their predictions through confidence-aware fusion at inference time. The supervised and SSL encoders are separately parameterized and need not share the same GNN architecture. We evaluate multiple GNN backbones and two distinct SSL objectives, DGI and GRACE, on four graph
benchmarks spanning temporal and cross-domain distribution shifts. Extensive experiments show that Co-Train consistently outperforms strong supervised OOD baselines, while Dual-Space
Retrieval achieves competitive performance as a flexible non-parametric alternative. Results across different backbones and SSL objectives, together with representation analyses and ablations, demonstrate that SSL representations provide complementary information to supervised representations and can improve OOD node classification across diverse settings.
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All authors Qingying Hao, Zikang Chen, Chuxuan Hu, Jinyuan Jia, Bo Li, Gang Wang, Carl Gunter
Artificial Intelligence
Learning to Outgrow a Theory: Experimental Discovery Beyond the Initial Hypothesis Space
SiYuan Ma, Albert Gao, Chunzheng Zhu et al. (+6 more)
Scientific discovery systems typically optimize experiments within a fixed hypothesis space. This creates a failure mode when all available candidates omit the same missing mechanism: candidate disagreement can collapse even while the model class is systematically wrong. We formulate experimental model-class revision, in which a discovery policy jointly proposes a structural edit and a diagnostic experiment that tests whether that edit is necessary. The method couples a class-level distinguishability objective, in which one shared parameterization must explain all selected experiments, with anytime-valid sequential evidence that triggers structural revision only after the current class is rejected. On 400 held-out controlled dynamical environments, the joint policy reaches 89.5% exact recovery with a budget of 32 real experiments, improving the strongest matched baseline by 10.0 percentage points while requiring fewer executed experiments and candidate fits. The learned revision-experiment pairing transfers across unseen mechanism combinations, held-out but expressible primitives, parameter extrapolation, and shifted experiment costs; when the true mechanism is outside the edit grammar, it detects library insufficiency in 88% of cases with a 5.5% false-support rate. Revision gains also transfer to ODEBench and ODEBase model-library tasks, as well as DiscoverPhysics worlds. These results support a view of scientific discovery in which deciding what mechanisms a theory should make expressible and where to collect evidence are treated as a single sequential decision problem.
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All authors SiYuan Ma, Albert Gao, Chunzheng Zhu, Xin Yan, Wenlong Zhang, Wenxin Zhang, Luqi Gong, Tianlin Li, Qixin Zhang
Audio and Speech Processing
A Novel Sentence Stress Detection Framework Leveraging Auxiliary Word-Stress Modeling and Loss Optimization
Tien-Hong Lo, Fong-Chun Tsai, Ting-An Hung et al. (+3 more)
Prosodic stress is a crucial aspect of automatic pronunciation assessment (APA), encompassing both sentence stress detection (SSD) and word stress detection (WSD). SSD highlights semantically salient words that shape discourse meaning, while WSD identifies the primary stressed syllable within each word to ensure lexical clarity. However, most prior work treats SSD and WSD as independent tasks, overlooking their shared reliance on prosodic cues such as pitch, duration, and intensity. To address this gap, we propose an effective SSD approach combining SSD with auxiliary WSD via a novel modeling paradigm. In addition, we introduce a word-span stress regularizer (WSR) that concentrates
token-level SSD probabilities within each stressed word span. Experiments on the TinyStress-15K
benchmark show that the proposed method outperforms strong baselines, with the complete configuration achieving the best SSD result.
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All authors Tien-Hong Lo, Fong-Chun Tsai, Ting-An Hung, Yu-Hsuan Hsieh, Yao-Ting Sung, Berlin Chen
Machine Learning
Stateless Language Agents: Scaling Long-Horizon Automated Research
Qizheng Zhang, Changxiu Ji, Isaac Sun et al. (+15 more)
Automated research systems increasingly run LLM
agents over long horizons, but more inference does not by itself produce more progress:
agents replay growing histories, duplicate one another's work, or stop experimenting while
token consumption continues. Yet most evaluations use short budgets or
benchmarks that saturate early, leaving these failure modes untested. We trace these failures to two choices: where research state lives and who decides what to try next. We introduce Stateless
Language Agents (SLAs), built on the principle of stateful search with stateless
agents: no
agent carries its conversation across invocations; instead, the harness owns the research state (candidate solutions and measured outcomes) and reconstructs a fresh and role-specific context for every invocation. What each
agent sees becomes an explicit design choice rather than a history that grows with the run. We implement this principle in the SLA framework, where a stateless Advisor reads harness-summarized evidence across search directions and assigns concrete experiments to parallel Workers. We evaluate SLA against three recent frameworks on software engineering, kernel optimization, and
algorithm design at budgets of up to one billion
tokens. SLA achieves the best final result on every task and reaches the strongest kernel baseline's final performance with over 84% fewer
tokens. Ablations from shared checkpoints show that focused contexts and explicit assignments each contribute to SLA's progress, with effects that can compound over full runs, while the Advisor consumes less than 0.6% of
tokens. These results argue for SLAs, which keep durable research state out of
agent conversations, and show that short evaluation horizons can misjudge research systems and their components.
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All authors Qizheng Zhang, Changxiu Ji, Isaac Sun, Yuetai Li, Shubhangi Upasani, Sherry Ruan, Boyuan Ma, Fenglu Hong, Vamsidhar Kamanuru, Yoonho Lee, Yuzhen Mao, Genghan Zhang, Rulin Shao, Qiuyang Mang, Andy Dimnaku, Changran Hu, Radha Poovendran, Kunle Olukotun
Astrophysics of Galaxies
Radiation pressure powers quasar broad absorption line winds but fails to drive galaxy feedback
Braden Seefeldt-Gail, Lluís Mas-Ribas and Norman Murray
Most cosmological simulations invoke
quasar-driven winds as the primary mechanism quenching
star formation in massive
galaxies, assuming wind momentum flux approaches the radiation-pressure maximum, $L/c$. Using 81,388 broad absorption line troughs from the SDSS-IV DR16
quasar catalog and detailed mock trough spectra, we detect line locking, a spectral signature of radiation-pressure-driven
outflows, in 60 to 70 percent of troughs, demonstrating that radiative line driving dominates wind acceleration and ruling out alternative mechanisms. Direct measurement of the wind energy budget yields a median kinetic luminosity of only 0.01 percent of bolometric luminosity, 50 times below the level required for effective feedback. Line-driven
quasar winds are therefore insufficiently powerful to quench
star formation, challenging the primary physical mechanism invoked for black hole feedback in
galaxy formation models.
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Machine Learning (Statistics)
Explicit Asymptotic Bounds for Sequential Calibration Beyond $T^{2/3}$
Eric Dai and Maxwell Fishelson
Probability forecasts are calibrated when predicted probabilities match empirical outcome frequencies: among events assigned a probability $p$, we'd hope that the fraction of positive outcomes is close to $p$. We study the problem of sequential
forecasting of binary outcomes. The classical $O(T^{2/3})$ bound on expected cumulative $\ell_1$-calibration error established by Foster and Vohra stood for over two decades until Dagan et al. reduced the exponent $2/3$ by an unspecified constant. We establish a new two-phase recursive labeling strategy for the sign-preservation-with-reuse game that yields the bound $O(n^{\alpha}t^\beta)$ for all choices of space and time. We then sharpen the reduction from upper bounds on sign preservation to calibration by modifying the equivalence of Dagan et al. to use only $O(\log T)$ instances of the sign-preservation-with-reuse game. This lets us establish an explicit bound of $O(T^{0.662942288})$, the first explicit exponent below $2/3$ for sequential calibration, by combining both improvements and choosing explicit feasible parameters.
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Information Retrieval
DBRAG: Multi-Table Retrieval-Augmented Generation for Complex Database Queries
Prince Larbi Ampofo, Ryoji Kubo and Djellel Difallah
Recent advancements in large
language models have introduced new capabilities for
reasoning over structured data, particularly through program-aided tools that can analyze tables. However, many existing methods address single-table scenarios or assume that the relevant tables are already provided. In practice, users often issue complex data exploration queries over entire databases, where relevant information may be distributed across multiple relations. In this work, we introduce DBRAG, a
retrieval-augmented generation framework tailored for multi-table question answering. DBRAG first retrieves candidate tables using an offline table index, enriches their summaries with query-relevant rows, and uses an LLM to rerank the candidates. A program-aided reasoner then selects the required tables and executes operations over their full contents, keeping the initial
prompt context compact. Experiments on the Spider, GeoQuery, and ATIS
datasets used in this study demonstrate improvements in table
retrieval and multi-table question answering.
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Robotics
TacZero: Training-Free Peg Insertion Using a General-Purpose Vision-Language Model with Tactile Feedback
Kazutoshi Tanaka
Robots that autonomously determine their actions from
language instructions and sensory observations could perform new contact-rich manipulation tasks without task-specific training or hand-designed rules. To perform these tasks,
robots must infer how objects contact one another and move as a result, then select actions. For contact inference and action selection, prior approaches involve designing estimation models and tactile feedback control laws, or learning models for object-motion estimation, action-outcome prediction, and action selection from tactile data. Instead, we propose TacZero, which uses a pretrained general-purpose
vision-
language model (VLM) to interpret visual and tactile observations and select
robot actions without additional tactile or manipulation training or task-specific rules for contact interpretation or action selection. TacZero provides the VLM with camera images,
robot state, and three-axis tactile responses represented as numerical values or vectors overlaid on the images. From these observations and interaction history, the VLM generates commands specifying target end-effector positions and gripper opening or closing, which a low-level controller executes. In real-world cylindrical-peg insertion experiments, TacZero succeeded in 15 of 20 trials with numerical tactile input, compared with 10 of 20 without tactile input. This study provides a concrete starting point for further research on contact-rich manipulation using general-purpose VLMs and highlights challenges in pursuing this direction.
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Artificial Intelligence
Explore, Then Commit: Measurement-Efficient Scientific Law Discovery with Language Models
Kautik Mandve and Dileepa Fernando
Scientific law discovery requires selecting measurements and converting evidence into a governing equation. We evaluate an explore-then-commit protocol in which a large
language model proposes hypotheses, a programmatic planner gathers measurements, and a fresh
prompt synthesizes the final law from fixed observations. The protocol combines structured probes, automatic numerical diagnostics, restricted measurement batches, and optional interpreter access. Across 576 NewtonBench trials, we compare eight configurations on 12 physics modules using GPT-4.1-mini and a medium-difficulty GPT-4.1 replication. On medium tasks, interpreter-enabled planners use 8.6 versus 22.5 measurements per trial for GPT-4.1-mini and 8.9 versus 43.0 for GPT-4.1. Their mean magnitude-based root-mean-squared logarithmic error falls from 2.514 to 0.202 and from 0.626 to 0.149, respectively. An additional audit retains incomplete and invalid submissions in a coverage-sensitive analysis. Observed symbolic-accuracy gains are less consistent across modules, and random acquisition is competitive with disagreement scoring. Measurement savings occur in every module, but unequal batch constraints prevent attributing them solely to acquisition quality. These results support the complete protocol as a promising measurement-efficient configuration, while leaving its causal components and generalization beyond noiseless direct-equation tasks unresolved.
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Human-Computer Interaction
Beyond screen time: Explaining cross-national differences in digital literacy through socioeconomic and psychological mechanisms
Hyejeong Lee, Daeyoung Ham, Suyoun Kim et al. (+1 more)
This study provides a structural explanation for cross-national variation in the relationship between screen time and digital outcomes. While prior research and large-scale assessments such as ICILS have documented inconsistent associations between screen time and digital competence, the mechanisms underlying these differences remain unclear. Using ICILS 2023 data, this study employs multigroup structural equation modeling to examine the relationships among socioeconomic status, screen time regulation, ICT self-efficacy, and digital literacy outcomes. Results reveal substantial cross-country differences in the effects of screen time regulation. In contrast, ICT self-efficacy emerges as a consistent and robust predictor across all countries. Moreover, screen time regulation influences outcomes indirectly through self-efficacy in some contexts but not others. These findings challenge the use of screen time as a standalone indicator of digital engagement and highlight the importance of psychological mechanisms. By integrating socioeconomic, behavioral, and psychological factors, this study advances a more nuanced understanding of digital competence and moves beyond quantity-based approaches to digital learning.
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All authors Hyejeong Lee, Daeyoung Ham, Suyoun Kim, Tiffany Emanuel
Information Theory
Joint Beamforming for Continuous Transmissive RIS-Enabled Multiuser Communications
Beining Han, Shumeng Zhang, Deyou Zhang et al. (+3 more)
This paper studies continuous transmissive reconfigurable intelligent surface (CT-RIS)-enabled multiuser downlink communications, where passive beamforming is characterized by a phase function defined over the continuous aperture. We employ a distance-dependent spherical-wave model for the base station (BS)-to-CT-RIS link and introduce a finite-path model based on field-response vectors for the CT-RIS-to-user links. The resulting weighted sum rate maximization problem jointly optimizes the finite-dimensional BS precoders and the infinite-dimensional CT-RIS phase function, giving rise to a mixed finite- and infinite-dimensional non-convex optimization problem. To address this problem, we develop an alternating optimization
algorithm that combines a closed-form BS precoder update with a functional phase update derived using the calculus of variations and minorization-maximization. Numerical results demonstrate that the proposed CT-RIS consistently outperforms the considered
benchmarks, with the performance gains attributed to spatially continuous phase control and a larger effective receiving area.
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All authors Beining Han, Shumeng Zhang, Deyou Zhang, Qingchao Li, Jun Liu, Chuang Shi