Artificial Intelligence
Jailbreak Context Lingers: Divergent Safety Routing and Its Cross-Task Predictability in Tool Agents
Xi Wang, Songlei Jian, Yiming Zhang et al. (+5 more)
As large
language models increasingly operate as tool-using
agents, post-jailbreak safety feedback is often assumed to serve as a reliable safeguard; however, how lingering jailbreak context shapes subsequent
agent behavior remains largely unexplored. To systematically examine this dynamic, we introduce a paired continuation framework across 192 parent tasks spanning 42 domains, evaluating 12,148 analyzed continuation pairs (curated from a 12,288-pair initially design) across eight diverse
agents. We find that identical safety feedback induces sharply model-dependent behavioral routing rather than uniform protection: redirecting unsafe trajectories toward legitimate completion (rescue), sustaining unauthorized execution (persistent unsafe), or triggering over-refusal on benign tasks (collateral loss). Through layer-wise activation patching, we discover a shared late-commit pattern where causal intervention effects surge sharply near the final layers (relative depths of 0.958–0.984) despite an over 30-fold variation in peak magnitude across architectures. Crucially, critical-layer representations correlate with macroscopic routing outcomes, and intervening at these layers causally alters concrete next-step tool actions. Building on this causal foundation, we test whether localized intervention-derived features can serve as predictive proxies for full-trajectory routing outcomes on unseen parent tasks under leave-one-parent-task-out evaluation, finding that they provide viable predictive signals in responsive
agents with peak ROC AUCs reaching 0.675 for rescue, 0.777 for collateral loss, and 0.702 for persistent unsafe. These findings establish a mechanistic lens and a predictive baseline for anticipating the safety and utility trade-offs of post-jailbreak feedback in autonomous
agents.
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All authors Xi Wang, Songlei Jian, Yiming Zhang, Bin Ji, Zhaoye Li, Ma Jun, Baosheng Wang, Jie Yu
Space Physics
P2D - A Two-Dimensional Multi-Spacecraft Solar Wind Persistence Model
Daniel Milošić, Manuela Temmer, Stephan G. Heinemann et al. (+2 more)
The background
solar wind is a key component in space weather
forecasting, as it contains geoeffective high-speed streams and provides the medium through which coronal mass ejections propagate and evolve in interplanetary space. Due to the
solar rotation and the slow
evolution of large-scale
solar wind structures,
solar wind properties exhibit an autocorrelation with a period of roughly 27 days, particularly at
solar minimum. We made use of this property to develop a
solar wind persistence model with input from multiple spacecraft (
Solar Orbiter, Parker
Solar Probe, STEREO-A, STEREO-B and the OMNI database). The model ballistically propagates in-situ data from the position of their measurement radially away from the Sun, as well as longitudinally with the
solar rotation rate, producing 2D maps of
solar wind parameters. These can be extracted at any point in the heliosphere for a
solar wind reconstruction. From Earth's perspective, the reconstruction performs with an MAE of 65.41 km/s and 3.51/cm^3 for speed and density, respectively. During high-speed streams, the peak hit rate is 50% and 48% for speed and density. It performs best during times when there are spacecraft located between Earth and Lagrange point L5. During these times, the mean absolute error of the predicted
solar wind speed decreases by roughly 35% in comparison to the
benchmark 27-day persistence model. Therefore, future L5 missions like Vigil are expected to provide a robust basis for reliable persistence forecasts.
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All authors Daniel Milošić, Manuela Temmer, Stephan G. Heinemann, Mathew Owens, Stefan J. Hofmeister
Robotics
Natural State-Prediction Accuracy can Hide Weak Controlled Responsiveness in VLA Readouts
Hyungjoon Kim, Wonbin Son, Mi Young Lee et al. (+2 more)
Accurately decoding object states from the internal representations of
vision-
language-action (VLA) models does not establish that the predictions respond faithfully to changes in the target physical state. In natural observations, object state,
robot configuration, occlusion, and task progress vary together, allowing contextual cues to contribute to prediction. In this paper, we introduce an evaluation framework that separates prediction accuracy, target-state responsiveness, and context stability using physically validated observations that cross target coordinates with
robot contexts. We demonstrate that high natural-trajectory accuracy can coexist with weak controlled target-state responsiveness in fixed representation-readout pairs. Comparisons and interventions involving representations, readouts, and training data show that the three properties provide distinct diagnostic information. Furthermore, adding responsiveness and context sensitivity to a failure predictor based on initial state error and physical variables reduces policy-failure prediction error on new initializations relative to the specified baseline while same-observation controlled MAE is also informative. These findings motivate evaluating target-state responsiveness and context stability alongside natural prediction accuracy, and examining their relationship to actual policy behavior and task outcomes.
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All authors Hyungjoon Kim, Wonbin Son, Mi Young Lee, Jun Young Lee, Seungmin Rho
Machine Learning
AgentPerfBench: A Benchmarking and Evaluation Suite for Inference Performance of Agentic LLMs
Cheuk Hang Lau, Zeyu Cao, Kevin Wong Cheuk Yin et al. (+6 more)
The optimization of LLM serving engines, such as vLLM and SGLang, is largely
benchmark-driven: optimizations, scheduling policies, hardware and system designs are all selected based on representative workloads. However, a significant mismatch has emerged in the agentic era. Existing
benchmarks primarily focus on simple single-turn chatbot workloads. LLM applications are increasingly agentic: coding
agents, terminal execution systems, and tool-use
agents issue multi-turn requests with growing context lengths. We introduce AgentPerfBench, a
benchmark suite for agentic inference. It uses real traces from agentic
benchmarks, such as SWE-Bench and TerminalBench, alongside standard chat baselines. This enables benchmarking of models on multi-turn tasks involving tool calling, skill utilization, and increasing context lengths. AgentPerfBench also samples from empirical distributions of input length, output length, and turn count derived from the real traces, generating representative synthetic profiles for cheap and accurate measurements on new hardware. In addition, we further find that several existing
benchmarks fail to accurately reflect real hardware performance for two key reasons: 1) they do not account for realistic context-length growth, and 2) they measure inference performance without operating at hardware saturation. We discuss these issues in detail and provide rich kernel-level Nsight Compute (NCU) traces to construct a new multi-dimensional roofline model that captures hardware-system limitations in both
memory bandwidth and
memory capacity footprint. The benchmarking suite then includes automated scripts to identify potential bottleneck conditions on emerging hardware when evaluated with diverse agentic traces. Together, these contributions quantify the chat-to-agentic gap in current inference
benchmarks and characterise per-kernel GPU resource utilisation via roofline analysis.
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All authors Cheuk Hang Lau, Zeyu Cao, Kevin Wong Cheuk Yin, Yao Lai, Haoran Wu, Nicholas D. Lane, Robert D. Mullins, Ilia Shumailov, Yiren Zhao
Computer Vision
V-Gym: Enhancing Agentic Visual Reasoning via Skill-Data Co-Evolution
Bei Yan, Yuecong Min, Jie Zhang et al. (+3 more)
Advances in
multimodal understanding,
reasoning, and tool use enable
agents to tackle increasingly complex visual
reasoning tasks. By distilling past execution experience into reusable skills,
agents can transfer lessons from both successes and failures into future
reasoning, reducing repeated errors and improving capabilities. However, limited experience may produce unreliable, poorly generalizable skills, while static
datasets may lack the targeted and diverse practice needed for refinement. To address this gap, we introduce V-Gym, an autonomous framework that iteratively co-evolves procedural skills and
multimodal practice data from execution trajectories. During skill
evolution, V-Gym analyzes trajectories to distill and refine hierarchical skills, updating procedural guidance and applicability conditions while retaining an update only if it improves validation performance. During data
evolution, V-Gym selects generation seeds by balancing data utility and exploration, then translates trajectory-identified bottlenecks into diverse, targeted practice data that expand the data bank after quality checks. The resulting practice outcomes feed back into subsequent skill updates, closing the loop for continual skill refinement. Experiments across diverse
multimodal reasoning benchmarks show substantial improvements over baselines with multiple backbone models. Its evolved skills generalize across domains and models, while evolved data support more effective skill refinement, enabling autonomous diagnosis, targeted practice, and continual self-improvement.
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All authors Bei Yan, Yuecong Min, Jie Zhang, Junqi Yang, Shiguang Shan, Xilin Chen
Machine Learning
SOLAR: A State-Driven Online Learning Rate Scheduler for LLM Pretraining
Qiulin Shang, Binyu Wang, Yongqi Qiao et al. (+3 more)
Learning-rate (LR) scheduling plays a central role in large
language model (LLM) pretraining, yet current practice still relies heavily on hand-crafted heuristics such as Warmup-Cosine-Decay and Warmup-Stable-Decay. Because these schedules are fixed in advance, they cannot adapt to evolving optimization dynamics. Online learned scheduling within the Learning to Optimize (L2O) framework offers a dynamic alternative, but remains brittle at LLM scale due to noisy signals, delayed feedback, and the risk of catastrophic divergence. We propose
SOLAR (State-driven Online Learning rAte scheduleR), a stabilized framework for reliable online LR adaptation.
SOLAR uses a base schedule as a reference and learns bounded, state-dependent residual corrections for individual parameter groups. Each correction re-anchors to the base at every step, allowing the policy to adapt the LR without relearning the warmup-decay profile. A lightweight state representation and progress-aware reward guide online learning, while a
Circuit-Breaker restores training after rare unsafe actions. Across autoregressive
language-model pretraining,
SOLAR improves final perplexity over tuned static schedules and automatic LR tuners for dense models from 60M to 1B, AdamW and
Muon, and two MoE settings up to 3B. Matched 130M controls show that adding base anchoring and action bounds improves a global PPO controller from 27.09 to 23.74 final PPL, while group-wise control reaches 22.87 on the same two seeds. A residual policy trained on a 60M proxy can also be frozen and reused at larger dense scales without target PPO updates, remaining effective across a fourfold base-LR range. These results establish
SOLAR as a practical learned LR controller for LLM pretraining.
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All authors Qiulin Shang, Binyu Wang, Yongqi Qiao, Songde Rao, Zhoutong Wu, Kun Yuan
Machine Learning
QuantForge: Discovering Residual Decompositions for MXFP4 Post-Training Quantization
Qiulin Shang, Zhoutong Wu, Jie Hu et al. (+1 more)
Four-bit post-training quantization can reduce the
memory demands of large
language models, but preserving accuracy under strict MXFP4 W4A4 requires coordinating several design choices. Coordinate transforms change block-encoding errors, which in turn affect the residuals propagated through the network. The useful algorithmic decomposition is therefore not fully known before search. LLM-driven program
evolution offers a way to explore these choices, but performance scores alone do not explain which design should change next. We introduce QuantForge, a PTQ discovery system that records competing explanations, selects controls that distinguish them, and checks that successor code implements the resulting conclusions. This residual compilation guides program revisions while retaining useful programs even when their original explanations are rejected. Remeasuring the revised program reveals the next error to address. This process discovers HiRes, a fixed MXFP4 quantizer that shapes coordinates, refines legal code assignments, and recovers errors along
attention and MLP paths. Each stage acts on residuals measured after the preceding stage has executed. Across seven tasks, HiRes achieves the lowest seven-model Robust Fit (0.09300) and the lowest quantized Fit-7 at 32B. In matched-budget comparisons of LLM-driven program
evolution, each with 240 evaluator calls, QuantForge reaches a held-out transfer target in six of eight runs, compared with three each for textual
memory and reflection
memory, and one for score-only
evolution, despite evaluating fewer new programs. These results show that QuantForge improves the discovery of transferable PTQ
algorithms by turning controlled evidence into subsequent program changes. We have open-sourced the QuantForge search framework.
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All authors Qiulin Shang, Zhoutong Wu, Jie Hu, Kun Yuan
Machine Learning
From Preference to Reciprocity: Decentralized Matching with Empirically Grounded LLM-agent Based Modeling
Wangxuan Fan, Xiaoyu Nie, Zhoutian Shi et al. (+5 more)
Bipartite matching is a fundamental problem in game theory and
market design. Classical approaches such as Gale–Shapley assume complete preferences and centralized computation, whereas many real-world matching processes are decentralized, asynchronous, and shaped by sequential interaction under limited information. We propose a dynamic bipartite matching framework that combines large
language model (LLM)
agents with contextual bandits. In a simulated Chinese marriage
market, economically grounded LLM
agents evaluate locally encountered candidates, while
agent-specific Logistic-UCB models learn reciprocal acceptance from realized proposal outcomes. The mechanism therefore separates two decisions—whom do I like? and who is likely to like me back?—without requiring ex ante
market-wide preference rankings. We first validate LLM-induced mate preferences against the empirical conditional-logit reference across multiple LLM backbones. In the $50\times50$ matching experiment, Bandit-UCB achieves the highest mean mutual welfare (56.01 versus 54.87 for Gale–Shapley), a smaller gender rank gap than the classical baselines, and the fewest blocking pairs among the LLM-ABM policies. Learned acceptance models show economically interpretable gender-differentiated associations, while counterfactual setups reveal no systematic unilateral advantage from prior search knowledge. Overall, these results support the advantages of decentralized matching with LLM-based behavioral modeling and online learning under incomplete information for economic simulation and computational social science research.
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All authors Wangxuan Fan, Xiaoyu Nie, Zhoutian Shi, Xiangcheng Meng, Shipei Zeng, Pin Gao, Yan Hu, Zhongxiang Dai
Computation and Language
Fair Fact-Checking: Closing the Cross-Lingual Gap in LLM Factual Judgement with RoSh
Muhammad Ahmad, Fatemeh Seyedin, Adrian Weller et al. (+2 more)
Misinformation on social media remains a critical problem, and more and more people settle it by asking a
language model instead of a fact checker. Whether models judge such claims reliably is debated; whether they judge them equally well in every
language people ask in has gone almost unasked. We test eight models from five families, 3B to 70B, on 1,500 encyclopedic factual claims that exist in identical form in eight
languages. English is judged better than every other
language on every model, and the gap is widest on the smallest ones, where Llama-3B on Arabic is no better than guessing. Existing remedies retrain on more
multilingual data or fit an unconstrained map between
language representations, and neither asks whether the model already holds the answer and simply fails to say it. It largely does: a linear probe recovers the truth from the very activations the model fails to express. We propose RoSh, a per-
language shift and rotation of the residual stream, computed in closed form at three layers, with no training and no weight modified. It improves every model and closes 75% of the gap on average, helping most where the model was worst: Arabic on Llama-3B goes from chance to nearly the English level, and a fifth fewer of the claims answered correctly in English are lost in translation. What remains is no longer a read-out failure: afterwards the head recovers as much of what is encoded outside English as it does in English. An unconstrained map fitted on the same pairs falls below the untouched baseline, so the orthogonality constraint is doing the work, and every model clears a scrambled-correspondence control and ten further controls. On the two
benchmarks of the closest inference-time method, latent-space intervention, run with its own data and metric code, RoSh's gains are five to thirteen times larger.
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All authors Muhammad Ahmad, Fatemeh Seyedin, Adrian Weller, Dongwon Lee, Mahmoudreza Babaei
Machine Learning
Learning What to Recall: Adaptive Multi-Cue Episodic Memory for World Models
Beomsu Kim, Chieh-Hsin Lai, Bac Nguyen et al. (+3 more)
World models predict future observations from current experience and actions, yet prediction can depend on observations seen far in the past. Episodic
memory preserves past observations for later recall; however, as
memory accumulates, it raises a fundamental question: which
memories are useful for the current prediction, and which available
retrieval cues should be trusted to find them? This is challenging because fixed criteria based on recency, pose overlap, or visual similarity can be unreliable across environments and queries. We propose Future-Aware Recall (FAR), a framework that learns episodic recall from future-aware predictive supervision and adaptive multi-cue scoring. During training, FAR measures predictive utility by the conditional log-likelihood of the realized future given recalled context, approximated by negative
diffusion prediction loss, and uses it to train a retriever that remains future-blind at inference. The retriever learns cue-specific relevance and automatically determines which available
retrieval cues, such as time, pose,
vision, and audio, to trust for each query when selecting
memories. Across three complementary settings, FAR outperforms hand-designed recall even with the same
retrieval cues, automatically adapts which available cues to trust, and recalls the right history as the world changes. Together, these results establish FAR as a flexible, principled approach to episodic
memory access in world models.
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All authors Beomsu Kim, Chieh-Hsin Lai, Bac Nguyen, Amir Bar, Jong Chul Ye, Yuki Mitsufuji
Number Theory
A new proof of two-color partition regularity of Pythagorean triples
James Leng
We give a new proof that if the natural numbers are colored in two colors, then there exists a monochromatic Pythagorean triple. The proof was found by ChatGPT Astra.
Classical Analysis and ODEs
Wave Packet Density in Local Smoothing: Refinements and Limitations
Xiangyu Wang
We study wave packet density estimates in the local smoothing problem for the wave equation. Building on the framework of Gan, He, Li, and Wu, we refine the broad-incidence argument to obtain an improved local smoothing range in even dimensions. We then investigate the limitations of wave packet density methods, showing that genuine
geometric obstructions already arise in the Euclidean setting.
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Robotics
HOI-Retarget: Contact-Centric Retargeting for Human-Object Interaction
Jihwan Shin, Adrià López Escoriza, Junzhe He et al. (+2 more)
Learning from demonstration (LfD) has enabled
humanoid robots to acquire diverse whole-body skills, but extending this paradigm to human-object interaction (HOI) is limited by the availability of
robot-compatible interaction references. We present HOI-Retarget, a contact-centric retargeting method that transfers HOI onto a
humanoid robot for large-scale motion-data generation. Its windowed trajectory optimization uses every labeled contact as a target in the object frame, balancing body tracking, foot support and smoothness under the
robot's kinematic limits. The method can augment a single demonstration across object sizes, absorb contacts reconstructed from
monocular video, and extend to several
robots manipulating one object. We publicly release the code and the retargeted motion
dataset.
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All authors Jihwan Shin, Adrià López Escoriza, Junzhe He, Matthias Heyrman, Marco Hutter
Machine Learning
FestDPO: Few-step Generator Alignment with Direct Preference Optimization
Jaewoo Lee, Kyuil Sim, Hyeongyu Kang et al. (+3 more)
Few-step generative models can generate high-fidelity samples within a few function evaluations. Despite this efficiency, generated samples may not exhibit desirable properties. When these properties are difficult to encode as an explicit reward function, direct preference optimization (DPO) can align generative models using pairwise preference feedback without training a separate reward model. However, extending DPO to few-step generative models is challenging because few-step generative models are generally implicit, making the likelihood evaluation required by DPO intractable. To address this challenge, we introduce Few-step DPO (FestDPO), an extension of DPO for few-step generative models that leverages nonparametric likelihood estimation from empirical samples. By exploiting the fast sampling capabilities of few-step generative models, our approach makes sample-based approximation of DPO loss computationally feasible. Furthermore, the sample-based formulation makes FestDPO agnostic to the model family and sampling procedure. Our toy experiment demonstrates that FestDPO matches the reward-tilted target distribution across four few-step generators. For real-world tasks, we evaluate FestDPO in two domains: text-to-image generation and
protein backbone generation. In text-to-image generation, FestDPO outperforms preference optimization baselines in both win rates against the base models and human evaluation scores. In
protein backbone generation, it achieves a higher $\beta$-sheet fraction and better structural designability than the baselines.
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All authors Jaewoo Lee, Kyuil Sim, Hyeongyu Kang, Kanghoon Lee, Woocheol Shin, Jinkyoo Park
Computer Vision
Evidence-Aligned Multimodal On-Policy Self-Distillation for Fine-Grained Visual Understanding
Nanxing Hu, Qiwei Yan, Jinchao Zhang et al. (+1 more)
Fine-grained visual understanding requires models to recognize small details within complex images.
Multimodal on-policy self-
distillation (OPSD) addresses this challenge by using a teacher conditioned on evidence-centered crops to supervise a student conditioned on original images along student-generated trajectories. Ideally, teacher corrections, the distributional changes from the student toward the privileged teacher, should be driven by task-relevant visual evidence. However, the designs that make the teacher effective also introduce other interference. Using a lagged or frozen teacher improves training stability but introduces a model-state gap from the evolving student, while cropping enhances task-relevant evidence but also loses the visual context. These two sources of interference make the teacher corrections not purely rely on the visual evidence. We introduce Evidence-Aligned
multimodal on-policy self-
Distillation (EAD), which retains the crop-conditioned teacher as the target but constructs a separate evidence reference for weighting the corrections. To exclude the effect of lagged model-state from this reference, EAD measures prediction changes using the current student. To avoid crop-induced context changes, EAD masks the evidence region in the original image while preserving the other visual context. The change from the student's masked-image prediction to its original-image prediction provides a controlled reference for the direction in which the visual evidence shifts the student's prediction. EAD weights each teacher correction by its cosine
alignment with the reference, i.e., retaining aligned corrections and downweighting the rest. Retaining only 6% of the supervision mass of dense OPSD, EAD consistently outperforms previous state-of-the-art methods.
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All authors Nanxing Hu, Qiwei Yan, Jinchao Zhang, Guoliang Kang
Statistical Methodology
DISCCO: Distance-Based Bayesian Spatial Clustering of Complex Objects with Node-Frailty Centrality
Srijato Bhattacharyya, Huiyan Sang and Bani Mallick
Clustering problems increasingly involve complex objects observed over space, such as distributions,
matrices, functions, images, or multivariate data, for which a scientifically meaningful dissimilarity between objects is often easier to specify and computationally more tractable than an object-response likelihood model. We propose DISCCO, a
Bayesian framework for clustering spatially indexed complex objects using only a pairwise distance
matrix and a spatial adjacency graph, with broad applicability and minimal user modeling requirements. The model combines a hierarchical distance-based likelihood accounting for within-cluster compactness and between-cluster separation with a random spatial graph partition prior, ensuring that posterior clusters are spatially contiguous. A key model feature is a set of node-specific frailty parameters that induce dependence among overlapping within-cluster distances and provide posterior summaries of object-level centrality or peripherality within each inferred cluster. We develop a partially collapsed Markov chain Monte Carlo
algorithm for posterior inference. Simulations with distribution- and
matrix-valued responses show that the proposed spatial distance-clustering framework improves region recovery relative to existing distance-clustering methods, while the frailty layer provides interpretable centrality summaries. Real applications to Houston Census Block Group racial-composition distributions and Western US county-level cancer mortality
matrices illustrate how the method recovers interpretable contiguous clusters and frailty-based centrality maps.
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Signal Processing
Hierarchical Clustering and Signal Denoising on Digraphs
Yi Wang, Sippanon Kitimoon, Hrushikesh N. Mhaskar et al. (+1 more)
In this paper, we propose a representation of a digraph (directed graph) as a Hermitian
matrix derived from its adjacency
matrix. This representation characterizes both the connectivity and the edge orientation of the digraph. Based on the spectral decomposition of the Hermitian
matrix, a digraph clustering
algorithm with $k$-means is introduced to produce a partition on the graph. Applying this
algorithm (bottom-up) recursively to a digraph with partially labeled vertices yields a spectral hierarchical digraph clustering (\myproj)
algorithm that produces consistent nested partitions of the digraph, or equivalently, a tree structure. Furthermore, based on the in-degree and out-degree of each cluster in the digraph clustering, a pair of hierarchical interval partitions (filtrations) can be derived in a top-down manner to produce a pair of nested
knot sequences. These
knot sequences facilitate the construction of multilevel spline quasi-interpolants, enabling a noisy graph signal to be decomposed into a coarse approximation and inter-level details, followed by adaptive thresholding and reconstruction. Experiments on synthetic and real-world digraphs demonstrate the superiority of our {\myproj}
algorithm for digraph clustering across diverse graph structural properties (homophily and heterophily) and supervision settings. Moreover, experiments on digraph signal processing using multilevel spline quasi-interpolants further demonstrate the effectiveness of signal recovery on digraphs in terms of RMSE and SNR.
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All authors Yi Wang, Sippanon Kitimoon, Hrushikesh N. Mhaskar, Xiaosheng Zhuang
Numerical Analysis
Fully Discrete Active Flux Methods for the Euler Equations: Exploring Different Reconstructions and New Multi-Dimensional Evolution Operators
Christiane Helzel, Mária Lukáčová-Medvid'ová, Amelie Porfetye et al. (+1 more)
We present new fully discrete Cartesian grid Active Flux methods for the two-dimensional Euler equations of gas dynamics. A key component of these methods is the
evolution operator used to evolve point-value degrees of freedom. Therefore, we derive several new, third-order accurate, multidimensional
evolution operators for point-value updates of the linearized Euler equations using the method of bicharacteristics. We also investigate how the reconstruction and choice of approximate
evolution operator affect the accuracy and stability of the resulting methods.
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All authors Christiane Helzel, Mária Lukáčová-Medvid'ová, Amelie Porfetye, Zhuyan Tang
Information Theory
Covert Communications over Enormous Fluid Antenna Systems (E-FAS): Impact of Channel Hardening on Detection
Farshad Rostami Ghadi, Damoon Shahbaztabar, Kai-Kit Wong et al. (+1 more)
Enormous fluid
antenna systems (E-FAS) enable guided surface wave (SW) propagation and induce an end-to-end channel that is conditionally complex Gaussian with a random covariance scale. This paper investigates the impact of this channel structure on covert communication in the presence of a passive warden. Under an equal power effective mode representation, the random channel scale follows a Gamma distribution, leading to a Bessel-K distribution for the effective channel power. We show that the optimal likelihood ratio test at the warden reduces to an energy detector and derive the corresponding false alarm and missed detection probabilities. These results are then used to characterize the achievable covert throughput under a prescribed detection error constraint. Numerical results show that, for finite observation intervals, finite mode E-FAS channels improve covertness relative to the fully hardened Rayleigh limit with the same average channel power. They further reveal an asymmetric role of channel hardening: stronger hardening can improve the legitimate link, whereas retaining channel scale fluctuations at the warden can enhance finite observation covertness.
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All authors Farshad Rostami Ghadi, Damoon Shahbaztabar, Kai-Kit Wong, Wei-Ping Zhu
Machine Learning (Statistics)
Two-Timescale Fine-tuning Provably Learns New Features for Two-Layer ReLU Networks
Etienne Boursier and Nicolas Flammarion
Fine-tuning pre-trained models on specialized tasks with scarce data is central to modern deep learning. Despite its empirical success, theoretical understanding of fine-tuning remains limited. We introduce a Gaussian multi-index setting to study fine-tuning from pre-trained weights, where the teacher network has $m+1$ features, $m$ of which are learned during pre-training and one of which must be learned during fine-tuning. For two-layer ReLU networks, we show that two-timescale training, i.e., updating the outer weights infinitely faster than the hidden ones, learns the new task-specific feature while preserving the pre-trained ones in the model representation. Moreover, only $\mathcal{O}(d)$ fine-tuning samples are required for this recovery, independently of the number of pre-trained features. In contrast, with random initialization, the same number of samples is insufficient to recover the target parameters. Our results therefore demonstrate that pre-training can induce an implicit bias with a clear statistical advantage over random initialization, enabling feature learning from scarce fine-tuning data.
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Robotics
On the Numerical Reliability of Differentiable Physics-Based Optimization for Robotic Material Manipulation
Xintong Yang, Minglun Wei, Yu-Kun Lai et al. (+1 more)
Differentiable physics is increasingly used in robotic material manipulation for system identification, trajectory or skill optimization, demonstration generation, and
robot or end-effector design. These applications depend on gradients propagated through long, contact-rich simulation rollouts. We study the numerical reliability of those gradients using two Material Point Method (MPM) system-identification
benchmarks derived from elastoplastic and granular manipulation. The
benchmarks provide controlled cases for three effects that also arise in broader differentiable physics-based optimization. GPU many-to-one sums whose order depends on thread scheduling changed long-horizon gradients and reversed the sign of one parameter gradient relative to a deterministic reference. Finite-difference checks became less reliable for longer rollouts because repeated-run loss variation grew much faster than the loss change produced by the tested parameter perturbations. Observation and loss definitions changed optimization behaviour and the solution preferred by an independent metric. These results motivate reproducible accumulation, finite-difference validation that compares perturbation-induced loss changes with repeated-run variation, and explicit reporting of objective construction when differentiable simulation is used for robotic optimization.
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All authors Xintong Yang, Minglun Wei, Yu-Kun Lai, Ze Ji
Game Theory
Settling the Complexity Landscape of Multi-Agent Contracts with Binary Actions
Michal Feldman, Maya Schlesinger and Shay Shani
We study the computational complexity of optimal contract design in the multi-
agent binary-action model, focusing on gross-substitutes reward functions and related classes. While additive rewards admit an FPTAS and general submodular rewards admit only constant-factor approximation, the complexity within the intermediate class of gross substitutes has remained largely open. We uncover a fine-grained approximation landscape within this class. We first show that the optimal contract problem is APX-complete even for OXS rewards - a strict subclass of gross substitutes rewards, ruling out a PTAS for gross substitutes unless $\mathsf{P}=\mathsf{NP}$. In contrast, for weighted matroid rank functions (WMRFs) - another natural subclass of gross substitutes - we obtain an EPTAS and show that no randomized FPTAS exists in general. We further identify the special case of partition (weighted) matroid rank functions, for which we obtain an FPTAS. This stands in contrast to the multi-
agent multi-action setting, where no PTAS exists even for unweighted partition matroid rank functions. Finally, we consider the broader class of ultra reward functions. While ultra rewards retain the tractability of gross substitutes in a related combinatorial contract model with a single
agent, we show a sharp contrast in the multi-
agent model: no
polynomial-time randomized
algorithm using value queries can achieve a $2^{o(n)}$-approximation in expectation. Together, our results reveal several qualitatively distinct computational regimes within and beyond gross substitutes, and identify submodularity as a crucial ingredient for the approximability of multi-
agent contracts.
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Representation Theory
Karol Janowicz
Let $k$ be a finite field. We consider the category of all functors $\mathcal{F}_n$ from the category of $k$-vector spaces of dimension $\leq n$ into the category of all $k$-vector spaces. We compare its derived category to the derived category of representations of the general linear group $GL_n(k)$ using the extension by zero functor procedure. We upgrade the well-known recollement of abelian categories into the recollement of the corresponding derived categories. Then we obtain some translations of the
cohomology the general linear group over finite field. We also compare $\mathcal{F}_n$ with the category of
polynomial functors $\mathcal{P}_{d,n}$, giving further reformulations over big finite fields.
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Performance
Tool Waiting and Re-arrival in Compile-Time-Static LLM Serving: Cost Mechanisms and Configuration Selection
Dongkyeom Jang, In-Nea Wang and Junho Jeong
In agentic LLM services, a session calls an external tool, waits for it, and re-arrives to continue inference. Statically compiled NPU serving can fix the batch bucket set, the maximum batch size, and the number of KV cache slots at compile time. We define such an environment as a compile-time-static serving substrate and analyze the execution-time cost that tool waiting and re-arrival incur in it. On a single LLM instance, we run synthetic workloads following a measured tool waiting time distribution and compare, on the same inputs, a baseline configuration with settings {1, 2, 4, 8}, 8, and 8 against configurations that change some of them. Because re-arrival times differ across configurations, we build a simulator that replays request processing in time order, select the candidate with the lowest predicted cost among 2,077 configurations, and validate it on new inputs. We identify three mechanisms: discrete batch
alignment, KV cache survival, and prefill interference. At a concurrency of 6, absent from the bucket set, tool waiting lowered the padding ratio (0.235 to 0.120) yet increased decode execution time 1.51-fold, so padding alone did not indicate cost. On new inputs at a concurrency of 8, where the baseline reused KV in 9 of 24 re-arrivals, enlarging the maximum batch size alone cut execution cost by 8.25%, and the selected configuration, which also adjusted the bucket set, by 9.72%. Where 17 of 18 re-arrivals were already reused, the effect was 0.59%. Compile-time configurations should thus be selected by diagnosing KV reuse loss and the resulting change in execution.
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Sound
Unsupervised Speech Enhancement via Drifting
Diego Caviedes-Nozal, Liang Xu, Rasmus Kongsgaard Olsson et al. (+1 more)
This paper addresses unsupervised
speech enhancement in the unpaired setting using drifting methods, where training relies on separate collections of degraded and clean audio without corresponding pairs. While recent drifting approaches enable unpaired training, they do so at a heavy cost: because the objective optimizes only a marginal prior over clean
speech, the enhancer gradually loses the input's linguistic content and speaker identity. To fix this, we introduce input-conditioned drifting. We preserve the pull of the clean corpus while re-tethering the output to the degraded input via two mechanisms: an anchor encoder supplies the missing likelihood by pulling toward the input's features, and a key encoder conditions the prior by re-weighting retrieved frames. Neither requires labels or paired data. Using a training-free encoder selection criterion, Word Error Rate on VoiceBank-DEMAND falls to 10.1% (unprocessed: 11.7%), speaker similarity recovers from 0.490 to 0.879, and the recipe transfers in part to dereverberation on WSJ0-REVERB: content improves, rendering quality does not.
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All authors Diego Caviedes-Nozal, Liang Xu, Rasmus Kongsgaard Olsson, W. Bastiaan Kleijn
Software Engineering
Codoku: Renewable Program-Reasoning Challenges for Frontier Coding Agents
Cong Li, Hao Sun, Zenan Li et al. (+1 more)
Existing program-
reasoning benchmarks ask large
language models to predict a program's behavior on a given input. Coding
agents break two assumptions on which these
benchmarks rest: an
agent can recover the answer by executing the program instead of
reasoning about it, and fixed task sets drawn from existing programs are increasingly exposed to contamination, yet costly to renew. We introduce Codoku (code sudoku), a renewable
benchmark in which a solver fills typed
cells in a partial program to satisfy global static and dynamic constraints, such as a prescribed control-flow graph and execution path. Because a partial program cannot be executed and valid fillings are sparse in an exponentially large space of interdependent choices, neither tool use nor enumeration can substitute for program
reasoning. Puzzles are synthesized from scratch via semantic reification, so fresh puzzles of controllable complexity can be generated on demand, each with a witness that guarantees solvability. We evaluate five frontier models on 300 puzzles through a coding
agent free to use any tool within a fixed budget. Small puzzles already challenge open-weight models, whereas even proprietary models solve only about half of the large ones. Codoku thus offers a renewable testbed for program
reasoning that can keep pace with rapidly improving coding
agents. GitHub: https://github.com/connglli/Codoku.
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All authors Cong Li, Hao Sun, Zenan Li, Zhendong Su
Computation and Language
Rewarding Novel Deductions: Solver-guided Process Supervision for Logical Reasoning
Muhammad Asif Ali, Wenqing Wang, Huan Wang et al. (+1 more)
Logical
reasoning remains a major challenge for large
language models (LLMs), particularly on structured problems that require precise constraint tracking, consistency preservation, and multi-step deduction. This challenge is especially acute for small-scale LLMs, which are more prone to producing inconsistent, redundant, or brittle
reasoning trajectories. Existing approaches for improving logical
reasoning largely optimize for final-answer correctness, providing only weak supervision over the intermediate
reasoning process. In this work, we propose SPRING: (Solver-guided Process Rewards for Novel LogIcal
ReasoNing Step Generation). SPRING uses an SMT solver as a training-time verifier of intermediate
reasoning steps to provide process-level supervision. It introduces the notion of a novel
reasoning step, namely, a step that is logically valid, consistent with the evolving
reasoning state, and not already implied by previously accepted non-contradictory deductions. Based on this solver-based assessment, it designs process rewards that encourage novel inferential progress while penalizing contradictory and uninformative
reasoning steps. Evaluation across three logical
reasoning benchmarks: ZebraLogic, AR-LSAT, and Knights and Knaves, and four LLMs shows that SPRING consistently outperforms base LLMs, outcome-only reward baselines, and Logic-LM. On ZebraLogic, SPRING improves puzzle accuracy by up to 49.71 and 15.43 points over the base LLM and strongest outcome-only baseline, respectively. On AR-LSAT, it improves overall accuracy by up to 64.43 and 11.39 percentage points over the base LLM and strongest outcome-only baseline, respectively. On Knights and Knaves, SPRING achieves up to 93.14 puzzle accuracy and 96.05 person accuracy.
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All authors Muhammad Asif Ali, Wenqing Wang, Huan Wang, Mohammad Raza
Quantum Physics
Depth-Optimal Quantum Compilation
Francisca Vasconcelos
We achieve the first constant-depth
circuit for arbitrary single-
qubit gate synthesis. Unlike prior approaches, the construction is fully
unitary and requires no pre-supplied catalyst. For any constant $\delta>0$, it $\varepsilon$-approximates an arbitrary single-
qubit gate using $O(\log^{1+\delta}(1/\varepsilon))$ clean ancillae, Hadamard and $T$ single-
qubit gates, $O(\log(1/\varepsilon))$-width generalized Toffoli gates, and sublogarithmic-width Fan-Out gates. We further eliminate Fan-Out entirely, showing that Hadamard, $T$, and generalized Toffoli gates alone suffice for constant-depth synthesis. When restricted to the standard bounded-width gate model, our construction has depth $O(\log\log(1/\varepsilon))$, and we prove a matching $\Omega(\log\log(1/\varepsilon))$-depth lower bound. Overall, we establish that $\Theta(\log\log(1/\varepsilon))$-depth is unavoidable with only bounded-width gates, yet allowing even logarithmic-width multi-
qubit gates suffices to achieve constant-depth synthesis. These results also reveal new structure in shallow
quantum circuit complexity. We give a depth-preserving real simulation of bounded-error decision computation, showing that every depth-$d$ QAC
circuit can be simulated in depth $O(d)$ using only Hadamard, $X$, and generalized Toffoli gates. Thus arbitrary single-
qubit rotations and complex amplitudes do not increase the bounded-error decision power of QAC, even at constant depth. In particular, this reduces the long-standing
conjecture Parity$\notin$QAC$^0$ to proving a Parity lower bound against
circuits consisting only of Hadamard, $X$, and generalized Toffoli gates. More generally, this real normal form exposes a direct correspondence between the standard shallow-depth
quantum circuit hierarchy and a hierarchy of Forrelation
circuits with restricted oracle families.
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Computer Vision
CapField-OPD: Learning Continuous Capability Fields via Joint-Anchored Multi-Teacher On-Policy Distillation for Flow Models
Pengyang Ling, Jiazi Bu, Yujie Zhou et al. (+6 more)
Reward-specialized post-training produces strong experts for flow-based generative models, while multi-teacher on-policy
distillation (OPD) consolidates their capabilities into a single student. Existing methods, however, route each
prompt to a single teacher according to its semantic category, implicitly binding the desired capability to
prompt content. This coupling makes capability invocation vulnerable to
prompt perturbations and prevents users from explicitly adjusting the strength of the desired capability at inference time. In this work, we introduce CapField-OPD, an OPD framework that integrates multiple teachers into a continuous capability field through explicit capability coordinates. We use teacher models as anchors to construct this field, with the coordinates determining how their outputs are combined. Each capability configuration thus receives a unique supervision target, and capability control no longer depends on
prompt semantics. Since the training anchors may not be optimal at inference time, we further profile the learned field on a small calibration set. The coordinate with the highest mean reward serves as the recommended default, while coordinates that are frequently optimal offer a promising candidate set for test-time scaling. Extensive experiments on compositional generation, text rendering, and visual aesthetics demonstrate that CapField-OPD consolidates multiple specialized teachers into a single student while preserving or surpassing their performance, reliably invokes the desired capabilities under semantics-preserving
prompt variations, and supports continuous capability control and coordinate-based test-time scaling.
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All authors Pengyang Ling, Jiazi Bu, Yujie Zhou, Yibin Wang, Zeqiang Lai, Xiaoxiao Ma, Yi Jin, Huaian Chen, Yuhang Zang
Hardware Architecture
Torch-PIM: Automated Profile-Guided PIM Offloading for PyTorch
Heeeon Lee, Hyunwoo Nam, Junyong Heo et al. (+6 more)
Modern deep learning (DL) workloads are limited by data movement, and processing-in-
memory (PIM) targets this bottleneck by placing compute units near the
memory. However, PyTorch and other DL frameworks lack compiler support for making this decision on the code they lower: existing offloading frameworks target hand-written C/C++ programs, while those that address DL fix the candidate set to a list of operator types before lowering. We present Torch-PIM, a compiler framework that uses profile-guided optimization (PGO) to decide host-versus-PIM placement over the loop nests that progressive lowering materializes. Every parallel loop nest the pipeline emits enters the candidate space, and each is assessed in two stages: the amount of work it carries, and its
memory boundedness. Every quantity the assessment consumes is profiled on the host or obtained from the multi-level intermediate representation (MLIR) of the code. Across PIM configurations of 32 to 128 cores, Torch-PIM's offloading decisions yield speedups of up to 8.6x on tensor operators, 2.9x on MLP, 4.4x on
Attention, 5.1x on GPT-J-6B, and 3.6x on LLaMA-7B over CPU-only execution.
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All authors Heeeon Lee, Hyunwoo Nam, Junyong Heo, Hyunmo Sung, Jay Hwan Lee, Yeonsoo Kim, Seongho Jeong, Shinhyung Yang, Bernd Burgstaller