Artificial Intelligence · Cross-listed from cs.CV
AutoRef: Harness Optimization for Agentic Multi-Reference Image Generation
Yuta Oshima, Ku Onoda, Yusuke Iwasawa et al. (+3 more)
Recent image generation models can take multiple reference images as input and combine them into a new image. However, multi-reference image generation remains challenging: models may omit or duplicate subjects from the references, or produce images in which multiple subjects appear unnaturally pasted. Recent work has proposed image generation
agents that combine image generation models,
reasoning models, and a harness, which is an executable program that specifies how reference images are interpreted, how generation is performed, how outputs are diagnosed, and how the final image is selected. In multi-reference generation, however, references play different roles and outputs must satisfy many criteria at once, such as fidelity to each reference and the naturalness of the whole image, so many parts of the harness could be improved, from how references are processed to how outputs are diagnosed. This makes it hard to predict which changes will improve performance and by how much, and good harnesses difficult to design by hand; indeed, human-written harnesses vary widely in performance. We therefore propose AutoRef, which optimizes the harness automatically while keeping both models frozen: a coding
agent iteratively rewrites the harness code. AutoRef separates the tasks whose feedback informs proposals from the tasks used to select candidates, and continues the search from a beam of the top-ranked harnesses on the selection tasks. Using this procedure, we discover AutoRef-Harness, which improves the open-weight FLUX.2 [klein] 4B from 5.72 to 7.37 on held-out four-reference tasks of the MultiBanana
benchmark, matching or exceeding proprietary models including Nano Banana Pro and GPT-Image-1.5. Without re-optimization, the same harness also improves results when the generator, number of references,
benchmark, evaluator, or
reasoning model differs from those used in the search.
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All authors Yuta Oshima, Ku Onoda, Yusuke Iwasawa, Masahiro Suzuki, Yutaka Matsuo, Hiroki Furuta
Artificial Intelligence · Cross-listed from cs.LG
Let the Neurons Die: Exploiting ReLU-Induced Model Degradation
Kexin Li, Wenjun Qiu, Joshua Abraham et al. (+2 more)
Rectified linear unit (ReLU) networks can suffer from dying
neurons, where units with persistently negative pre-activations produce zero outputs, blocking gradients through their activations. To exploit this failure mode, we present three training-time availability attacks based on data ordering and poisoning. We begin with the basic dynamic data-ordering attack (DOA), which greedily constructs a training prefix by selecting the next example that minimizes the target layer's post-update weight sum, aiming to push ReLU units toward negative pre-activations without modifying training samples or labels. We then develop two poisoning attacks, IG-DOA and IG-SKA, which use gradient inversion to synthesize class-conditioned samples by matching reference gradients in adverse model states constructed through data ordering or soft knockout, respectively. Soft knockout rearranges weights across adjacent layers to concentrate negative contributions. On a fully connected ReLU network trained on MNIST, ordering 100 of 60,000 training examples reduces test accuracy from 96% to 95% after only five epochs. Adding 200 poisoned samples from a single class reduces test accuracy to approximately 86-88% after five epochs in most evaluated conditions, compared with approximately 96% under clean training. These results demonstrate that ReLU-targeted data ordering and poisoning can impair learning without directly modifying the victim model's parameters.
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All authors Kexin Li, Wenjun Qiu, Joshua Abraham, Aditi Maheshwari, David Lie
Artificial Intelligence · Cross-listed from cs.CL
Beyond Token Scale: Chunk-Level Sparse Autoencoders for Reliable Semantic Feature Discovery
Xu Wang, Yifan Yang, TingHao YU et al. (+1 more)
Sparse autoencoders (SAEs) expose features that help us understand and steer
language models, but faithful reconstruction does not guarantee informative concepts.
Token-level objectives reward lexical and formatting details alongside semantic content, all competing for a limited sparse budget. We introduce a family of chunk-level SAEs that encode mean-pooled activations over chunks, each a contiguous span of
tokens: Mean-Chunk reconstructs the observed chunk, Cross-Chunk predicts an independently processed neighbor, and Joint-Chunk combines both targets. These designs separate the effect of a larger observation unit from that of predicting information shared across passages. With matched training data, chunk-level SAEs remain powerful interpretability tools while learning reliable semantic features that capture high-level concepts and respond selectively to relevant content. Their strengths are complementary: Mean-Chunk improves high-level feature discovery,
reasoning detection beyond surface cues, and steering; Cross-Chunk leads document
retrieval and classification transfer while producing selective, persistent features. Changing what an SAE sees and predicts yields reliable semantic features for more meaningful tasks. We demonstrate their practical value through gains across downstream tasks such as
retrieval,
reasoning detection, and steering.
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All authors Xu Wang, Yifan Yang, TingHao YU, Difan Zou
Artificial Intelligence
MechBench: Can AI Scientific Agents Discover Mechanisms Beyond Phenomenal Laws?
Zihan Yu, Jiadong Zhang, Jialin Cheng et al. (+2 more)
Scientific discovery requires not only recovering mathematical laws that describe observable behavior, but also identifying the mechanisms that generate them. Existing
benchmarks for symbolic regression and scientific
agents primarily evaluate phenomenal-law recovery, leaving mechanism discovery largely untested. We introduce MechBench, a
benchmark that explicitly separates these two capabilities. Each task is defined by a mechanistic model, a structured set of scientifically meaningful relations whose joint consequences entail an observable phenomenal law, while
agents receive only observational data and scientific context. We evaluate mechanism recovery through mechanism probes, which query internal scientific consequences that cannot be inferred from the phenomenal law alone. To reduce reliance on memorized textbook mechanisms, we construct unfamiliar variants through controlled, scientifically interpretable mutations of canonical mechanisms, and screen for mechanistic indistinguishability to exclude ambiguous instances admitting comparable competing mechanisms. Experiments across representative scientific
agents reveal a substantial phenomenal–mechanism recovery gap: for Codex with GPT-5.6-sol, phenomenal-law accuracy reaches 35.00% on the Core-set while mechanism accuracy is only 13.75%, with mechanism recovery failing in 64.29% of cases where the phenomenal law is correctly recovered. The gap widens as mechanisms become increasingly mutated, and even providing the correct phenomenal law leaves mechanism recovery below 50%. These results reveal a substantial generalization gap in mechanistic
reasoning and establish mechanism discovery as a distinct challenge beyond recovering observable scientific laws.
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All authors Zihan Yu, Jiadong Zhang, Jialin Cheng, Jingtao Ding, Yong Li
Artificial Intelligence · Cross-listed from cs.LG
Improving Generative Model Self-Training with Geometrically Modified Outputs
Patrick Batsell, Thomas Walker and Richard Baraniuk
Self-training generative models - the continued improvement of a model using its own outputs - is becoming increasingly important as high-quality training data becomes scarce. However, naively finetuning on model-generated samples leads to degradation through model collapse and the model autophagy disorder. Negative-guidance self-training methods turn this degradation into a useful signal, using a model finetuned on its own outputs to guide the original model toward improved generation. Existing methods, however, take the negative signal in standard model outputs as given. We instead ask whether this signal can be explicitly strengthened. We introduce Geometrically Modified Outputs (GMOs), which reweight the singular values of the generator's input-output Jacobian to increase the influence of its leading singular directions. This
geometric modification amplifies the mode-seeking behavior and distortions of standard outputs, providing a stronger and more targeted negative signal for self-training. Across a range of one-step generative models, GMOs consistently improve the performance of negative-guidance methods, including Neon and SIMS, compared with using standard model outputs.
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Artificial Intelligence · Cross-listed from cs.CV
SolveEdit: Benchmarking Visual Problem Solving in Generative Models
Wenjie Shu, Yexin Liu, Harold Haodong Chen et al. (+9 more)
Machine intelligence is often evaluated through abstract
reasoning problems, yet many real-world problems are visual, such as arranging objects, repairing layouts, or tracing routes. Solving these problems requires understanding a scene, inferring what must change to achieve a goal, and realizing that change without disturbing unrelated content. However, existing
benchmarks mainly evaluate perception, generation, or explicitly specified transformations, leaving goal-driven visual problem solving underexplored. To bridge this gap, we introduce SolveEpIT, a
benchmark for visual problem solving through scene transformation. Given an image and a goal, a model must infer a valid transformation from the request, the scene, or a visually expressed rule, then execute it while preserving unrelated content. SoLvEEDrr contains 2,728 cases. Atomic transition contracts specify required and protected conditions, enabling SoLvEScoRE to measure completion and unintended changes without a single reference output. The strongest evaluated model achieves only57.0% SolvEScore. We further introduce SolveEdiT-PLAN, a two-stage visual planner that instantiates the transition before generation. Under matched single-generation evaluation, it improves SoLvEScoRE by 9.1 points on average across three tested generators, including a gain from 57.0% to 71.6% for GPT-Image-2, without modifying the editor.
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All authors Wenjie Shu, Yexin Liu, Harold Haodong Chen, Xuerui Qiu, Zehan Wang, Yidi Zhang, Yizhan Chen, Zunwei Wang, Minghao Liu, Qi Chen, Harry Yang, Xiaogang Xu
Artificial Intelligence
SRHarness: A Harness for Agentic Symbolic Regression
Zihan Yu, Shixuan Zhou, Hao Huang et al. (+2 more)
Recent agentic symbolic regression approaches increasingly rely on large
language models to analyze data, select scientific operations, and refine hypotheses over long search trajectories. In such systems, performance depends not only on the underlying model and search strategy, but also on the runtime infrastructure that supports scientific search. We introduce SRHarness, a domain-specific harness for agentic symbolic regression built around three mechanisms: composable scientific actions that provide a common interface over raw, transformed, and candidate-derived quantities; persistent scientific state that retains evaluated hypotheses and exposes compact model-facing views; and trajectory lifecycle management that coordinates continuation, branching, restart, and termination. On LLM-SRBench, SRHarness consistently improves both numerical generalization and symbolic recovery under matched LLM backbones. With DeepSeek-v4-flash-0731, it achieves 93.69% symbolic accuracy on LSR-Transform, compared with 62.16% for SR-Scientist, and retains 72.97% accuracy on an anonymized variant that removes scientific descriptions and variable semantics, versus 39.64% for SR-Scientist. Under the same DeepSeek-v4-flash-0731 backbone, SRHarness also substantially outperforms Codex (72.97% vs. 20.72%) and reaches performance comparable to Codex with GPT-5.5, while simply providing Codex with the same scientific tools does not reproduce this advantage. These results show that effective agentic symbolic regression depends not only on models or tools, but also on structured runtime support for organizing scientific actions, accumulated hypotheses, and long-horizon search.
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All authors Zihan Yu, Shixuan Zhou, Hao Huang, Jingtao Ding, Yong Li
Artificial Intelligence · Cross-listed from cs.CV
From Scores to Samples: Elastic Forcing for Autoregressive Video Generation
Chi Zhang, Yueyi Liu, Shi Haoyang et al. (+5 more)
Few-step autoregressive
video generation commonly relies on Distribution Matching
Distillation (DMD), requiring a bidirectional
diffusion teacher and an online fake-score model. We instead learn the rollout distribution directly from reference
videos, eliminating both score models during post-training. Our framework minimizes maximum mean discrepancy (MMD) in frozen self-supervised
video representation spaces, using a hybrid Nyström–Monte Carlo
estimator to balance approximation bias and sampling variance.
Memory-efficient replay and gradient subsampling make this objective practical. Using the same architecture and initialization as Self-Forcing, our 1.3B model improves the VBench Total score from 83.80 to 84.64 while retaining 17 FPS. Removing auxiliary score models also enables 14B post-training on eight H200 GPUs. Beyond
distillation, learning from reference
videos enables the acquisition of new visual styles, semantic concepts, and spatial priors without a target-specific
diffusion teacher.
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All authors Chi Zhang, Yueyi Liu, Shi Haoyang, Ruichuan An, Haoyu Li, Yuhang Wu, Sen Cui, Miao Liu
Artificial Intelligence · Cross-listed from cs.AR
Analog Computing revisited: A fully analog and minimalistic Damage Detector for Ultrasonic Testing enabling Material-Integrated Structural Health Monitoring
Stefan Bosse
Ultrasonic Testing (UT) is commonly used to detect damage in structures, e.g., metal plates. A sensor acquires Ultrasonic waves, e.g., by using PZT transducers. The time-resolved sensor signal must be processed with analog electronics, e.g., amplified and filtered. Commonly a digitalization follows using an Analog-to-Digital converter, finally processing the digital sensor signal, applying digital signal processing, feature extraction, and Machine Learning by using powerful microprocessor systems. The disadvantages of digital processing systems are their high number of transistors (microchip area), energy consumption, state-dependent processing and therefore sensitivity to energy supply interruption. Beyond silicon electronics, printed organic electronics gains interest. But printed electronics is still limited to low transistor and electronic component counts (typically 100). We will investigate and demonstrate a fully analog signal processing and feature extraction system consisting of an analog
Hilbert transform deriving the signal envelope, simple analog arithmetic calculations for feature extraction, and finally damage classification and regression using an analog Artificial Neural Network. We expect a full damage detection system with less than 100 transistors. We will test our damage detection system with PZT transducer signals from Steel plates with circular defects. The focus of this work is the analog computation of the signal envelope (using all-pass filter networks for approximation of the
Hilbert transform) and the analog feature extraction as well as the prediction of damage, forming an analog computer which can perform in-sensor computation, computing without a digital computer.
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Artificial Intelligence · Cross-listed from cs.CL
Spontaneous Context Restoration: How Language Models Recover from Corrupted Inputs
Pranjal Garg and Jacob Beck
Language models sometimes produce correct outputs even when their inputs are corrupted by deletion, replacement, or misspelling. We study the internal processes accompanying this behavior, which we call context restoration, in controlled
attention-only
transformers and five pretrained LLMs (1B-32B parameters) across arithmetic, reading comprehension, and multiple-choice
reasoning tasks. In the
attention-only
transformers, restoration emerges spontaneously despite training exclusively on clean sequences, without corruption training or an explicit denoising objective. We find that context restoration follows a two-phase process: early layers localize effects associated with repair at corrupted positions, while later layers accumulate these effects at uncorrupted positions through the residual stream and ultimately concentrate them at the output position. Repair outcome is predictable from hidden states: cosine
alignment with the clean state is highly predictive in
attention-only models, while linear probes recover additional information in pretrained LLMs. A linear probe using only the corrupted
prompt's first-block hidden state predicts failure with mean ROC-AUC 0.78. This enables failure triage under matched or even partially shifted deployment conditions and may reduce unnecessary verification or computation. Failed examples also show substantially greater nonlinearity along corruption directions. Moderate-corruption finetuning increases corruption tolerance while simultaneously reducing displacement-normalized linearization error, associating improved robustness with a more nearly linear response to corruption.
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Artificial Intelligence
Why Deterministic PRM Guidance Underperforms in Discrete Diffusion Reasoning
Yan Zhan, Shaobo Liu and Zhijun Gao
Discrete
diffusion language models (dLLMs) expose a denoised solution at every step, which makes process reward model (PRM) guidance look like a way to spend compute at test time. We show that once denoising, PRM scoring, and outcome reward model (ORM) scoring are charged in the same budget of forward passes, its deterministic form loses to a much simpler baseline. Our PRMs score intermediate denoising states and are trained on the correctness of the final answer. On Dream-v0-Instruct-7B with 8 candidates per GSM8K problem, keeping the candidate with the highest PRM score at every scoring step reaches 65.18%, while independent sampling plus an ORM reranker trained for the task reaches 75.13%. The gap grows to 12.69 percentage points (pp) with 32 candidates, and is 9.85 pp on MATH and 12.16 pp on MBPP. We trace it to two separable failures. First, guidance prunes on a weak signal: on GSM8K, PRM ROC-AUC falls from 0.77 to 0.54 as the mask ratio rises, a decay that persists when states are relabeled with fresh rollouts, and
pruning lowers the best accuracy reachable from the candidate pool from 81.05% for independent samples to 67.30%. Second, on GSM8K and MATH, the PRM is a poor final judge: a sequential Monte Carlo sampler at the same budget restores that ceiling to 77.89%, yet selecting with the PRM gives 65.48%, on par with deterministic guidance, while a PRM retrained on final states matches the ORM on identical candidates. MBPP separates the two: there the PRM reaches 65.47% when reranking finished programs, on par with the ORM, but 50.88% when it guides denoising. The results point to two targets for dLLM guidance: keep correct partial solutions alive through early denoising, and leave the final choice to a verifier trained on final states. We release the corpus of denoising states with outcome labels and evaluation toolkit for reproducible comparisons at matched compute.
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Artificial Intelligence · Cross-listed from cs.RO
Rethinking Causal Action Tokenization with Conditional Annealing in Flow Matching
Chenyu Zhang, Yuhang Cao, Daru Du et al. (+8 more)
Autoregressive
Vision-
Language-Action (VLA) models offer a scalable path to
robot learning, yet existing action tokenizers treat tokenization as a compression problem, producing representations that are semantically misaligned with the autoregressive backbone. We propose CATok, a causal action tokenizer that reframes tokenization as a causally structured generative process. CATok introduces a conditional annealing mechanism that extracts action
tokens by progressively annealing a flow-matching process: each
token is conditioned on all preceding
tokens and encodes the residual reconstruction signal at a specific noise level, establishing a coarse-to-fine causal
token space whose generative semantics are structurally aligned with autoregressive modeling. A
token-conditioned flow-matching decoder built on
Multimodal Diffusion Transformer (MMDiT) reconstructs continuous action chunks from these discrete
tokens with the precision of hybrid
diffusion-head architectures. This discrete bottleneck enforces knowledge insulation by design, cleanly separating high-level semantic
reasoning from low-level motor execution without requiring explicit
attention masking. Extensive evaluations across three simulation
benchmarks and real-world robotic manipulation tasks demonstrate that CATok consistently surpasses existing tokenization methods in both reconstruction fidelity-compression tradeoff and inference efficiency, while improving VLA task success rate and training efficiency, establishing a high-performance, scalable foundation for purely autoregressive VLA systems.
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All authors Chenyu Zhang, Yuhang Cao, Daru Du, Yingxi Lu, Jing Shao, Ruoqu Chen, Jiajun Liu, Liu Cao, Yicheng Liu, Hang Zhao, Mengdi Xu
Artificial Intelligence
A.D.A.M.O. (Agent for language-Driven Actions with Multimodal Observations): A Visual-Symbolic Framework for Virtual Humans
Alessandro Emmanuel Pecora, Stefano Calzolari, Francesco Strada et al. (+1 more)
Creating believable vh requires the coherent integration of perception,
reasoning, and action mediated by
language. A central challenge is to combine these components into a control loop grounded in interactive 3D environments. To this end, we present A.D.A.M.O. (
Agent for
language-Driven Actions with
Multimodal Observations), a visual-symbolic framework for
language-driven vh that leverages a pretrained vlm with tool calling to unify perception,
reasoning, and action within a single control loop. A.D.A.M.O. maintains a dual visual-symbolic world model that combines
egocentric visual input and synchronized symbolic state to support grounded task-oriented behavior from natural
language prompts. To support diagnostic evaluation, we introduce a controlled task suite organized by a cd taxonomy that breaks down spatial tasks into procedural and linguistic complexity. Experiments in controlled scenes show that semantic labeling strongly influences task completion and failure modes, reducing perceptual ambiguity while shifting failures toward downstream execution, whereas
reasoning errors remain comparatively rare.
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All authors Alessandro Emmanuel Pecora, Stefano Calzolari, Francesco Strada, Andrea Bottino
Artificial Intelligence · Cross-listed from cs.LG
CLIMB: A Clinical Multimorbidity Benchmark for Diagnosing Co-occurring Conditions through Multiturn Conversations
Yusuf Kesmen, Aniruddha Mukherjee, Yena Chang et al. (+7 more)
Patients often have several co-occurring clinical conditions, and the findings needed to identify and disambiguate them emerge over the course of a consultation. Evaluating clinical
reasoning in this setting requires both multi-turn interaction and multi-label diagnosis. We introduce CLIMB, a
benchmark in which a doctor model interviews a simulated patient to recover a ground truth set of co-occurring clinical conditions. Cases are synthesized from clinical decision
algorithms and diagnostic
datasets, grounding multimorbid presentations in structured clinical knowledge. Across six frontier and open models, none recovers the exact set of conditions in more than 10% of interactive cases. Diagnostic performance declines when conditions co-occur, even when models receive the full clinical record and the true number of conditions. Interaction reduces performance further. In controlled experiments, models behave like single-hypothesis trackers: they anchor on the diagnosis suggested by the opening findings, keep questioning around it, and recover a second condition mainly when a finding in view points to it. Questioning them further does not complete the set but adds mostly wrong diagnoses. We formalise this pattern with a theoretical reference model of single-hypothesis tracking. The
benchmark, generator, and evaluation code are available at https://anonymous.4open.science/r/CLIMB-8340.
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All authors Yusuf Kesmen, Aniruddha Mukherjee, Yena Chang, David Sasu, Trevor Brokowski, Alexandra V. Kulinkina, Kristina Keitel, Akhil Arora, Lars Henning Klein, Mary-Anne Hartley
Artificial Intelligence
AutoBCI: Forecast-Guided Agentic Neural Architecture Discovery for EEG-Based Brain–Computer Interfaces
Muyun Jiang, Yi Ding, Wei Zhang et al. (+9 more)
EEG-based
brain-computer interfaces support a broad range of applications, yet designing decoding architectures that perform well across diverse tasks remains challenging. We introduce AutoBCI, an agentic framework in which a Designer
Agent and a Forecaster
Agent support the discovery and selection of
EEG decoding architectures across tasks. The Designer
Agent performs Pool-Guided Architecture Discovery (PGAD), generating and refining architectures through training and validation across multiple
EEG tasks, such as
emotion recognition, motor imagery, and sleep staging. The Forecaster
Agent performs Performance Estimation from Early Knowledge (PEEK), using architecture code, the training protocol, and early learning curves to predict full-budget validation performance and select promising candidates for continued training. Across 14
EEG datasets spanning motor imagery,
emotion recognition, and sleep staging, we evaluate AutoBCI with six LLMs, including Opus 5.5 and GPT 5.6 Sol, and compare the architectures selected by the search procedure against ten baselines: six conventional
EEG models and four foundation models. The architecture discovered by AutoBCI with Claude Opus 5.5 achieves 64.16% average test balanced accuracy (bAcc), compared with 63.87% for REVE, the strongest baseline on this metric. Using ten observed epochs, PEEK reduces mean absolute error in predicting average validation bAcc from 2.20 to 1.36 percentage points, a 38.1% reduction relative to the best-observed-score baseline.
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All authors Muyun Jiang, Yi Ding, Wei Zhang, Jinbo Chen, Chenyu Liu, Zhenjie Yang, Yuxin Li, Jingyuan Chen, Yuhao Lu, Yong Li, Shuailei Zhang, Cuntai Guan
Artificial Intelligence
Just Initialize: A Training-Free Initialization Component for Large-Scale Routing Optimization
Jiale Zhao, Sirui Mao, Zimu Chen et al. (+5 more)
Large-scale routing problems are difficult to solve efficiently as their search spaces grow rapidly with problem size. Existing approaches primarily improve the optimization procedure itself, often at increasing computational cost. We instead shift the focus to a useful initialization that can be refined into a high-quality solution with limited downstream refinement. We propose Just Initialize, a training-free and solver-agnostic initialization component for large-scale routing optimization. Just Initialize compresses a large routing instance into a compact surrogate space, optimizes its global routing structure, and recovers the resulting solution as an optimization-friendly starting point in the original space. Extensive experiments on Traveling Salesman Problems (TSPs), Capacitated Vehicle Routing Problems (CVRPs), Vehicle Routing Problems with Time Windows (VRPTWs), and Prize-Collecting Traveling Salesman Problems (PCTSPs) demonstrate that Just Initialize achieves high-quality solutions comparable to or better than state-of-the-art methods while substantially reducing computational cost across instances ranging from 1K to 100K nodes, including an average speedup of approximately 70$\times$, sub-second runtimes on 10K-node instances, and runtimes within tens of seconds on 100K-node instances.
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All authors Jiale Zhao, Sirui Mao, Zimu Chen, Wentao Yang, Zihan Wang, Xuefeng Huang, Junji Cheng, Liyuanjun Lai
Artificial Intelligence · Cross-listed from cs.LG
Riccati State Space Models: Non-iterative Parallelization for Nonlinear Sequence Modeling
Mónika Farsang, Ramin Hasani, Daniela Rus et al. (+1 more)
State space models (SSMs) achieve efficient sequence processing because their affine state updates are closed under composition and can therefore be evaluated with an associative parallel scan. Nonlinear recurrent models can provide richer, state-dependent dynamics, but generally lose this compositional structure: parallel evaluation then requires iterative methods that repeatedly linearize and scan the recurrence. We ask, what state-dependent nonlinear dynamics can be designed to remain exactly composable? We answer by introducing RiccatiSSM, a nonlinear SSM, in which each state dimension follows an input-conditioned Riccati differential equation. Its quadratic state dependence makes the local Jacobian explicitly state-dependent, while its exact per-step flow under piecewise-constant inputs is a Möbius transformation. Since Möbius maps are closed under composition and compose through $2\times 2$
matrix multiplication, the complete nonlinear state trajectory can be evaluated exactly with a single associative parallel scan, without iterative linearization. We further derive a constrained parameterization that ensures bounded, contractive dynamics, and avoids poles in the fractional-linear state update. Across long-sequence classification, regression, and
forecasting tasks, RiccatiSSM achieves competitive predictive performance while reducing runtime by $22{-}33\%$ compared to the nonlinear LrcSSM under matched architectures. These results demonstrate that state-dependent nonlinear dynamics can retain exact composability and be evaluated efficiently within a single parallel scan.
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All authors Mónika Farsang, Ramin Hasani, Daniela Rus, Radu Grosu
Artificial Intelligence
Building Transformation Layers for Riemannian Neural Networks
Ziheng Chen
Recently, deep neural networks on
manifold-valued representations have garnered significant
attention across various machine learning applications. One recent focus is the generalization of Euclidean fully connected (FC) and convolutional layers to non-Euclidean geometries. However, previous approaches typically focus on a few selected
manifolds and rely on specific properties of the target
manifold. In contrast, this work proposes a framework for constructing FC and convolutional layers over computationally tractable Riemannian spaces. This framework incorporates several previous FC layers across different geometries as special cases and is instantiated on ten representative
manifolds, including three
hyperbolic models, five geometries of the symmetric positive definite (SPD)
manifold, and two Grassmannian perspectives. Experiments on different
manifolds demonstrate the effectiveness and applicability of our approach. Code can be found at https://github.com/GitZH-Chen/RieTrans.
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Artificial Intelligence · Cross-listed from cs.LG
ReSPO: Reshaped Sequence Policy Optimization for Gradient Starvation in Off-Policy Learning
Yihang Chen, Yuanhao Ban and Cho-Jui Hsieh
Reinforcement learning from verifiable rewards (RLVR) frequently reuses rollouts across multiple policy updates, increasing the mismatch between the current policy and the data-generating policy. We identify a sign-dependent gradient starvation problem in clipped policy optimization: clipping suppresses under-generated positive responses at the low-importance-weight tail while permitting severely over-generated negative responses to dominate the high-weight tail. To address this, we propose ReSPO (Reshaped Sequence Policy Optimization), which replaces clipping with a smooth, two-branch sequence-level kernel derived from an $\alpha$-divergence variational objective and an exponential variance-control tilt. The positive branch preserves a nonzero gradient weight for under-generated positive responses, while the negative branch suppresses heavily over-generated negative responses. We demonstrate that ReSPO effectively learns from long positive
reasoning trajectories during early training, even when accumulated policy drift relegates them to the low-importance-weight tail. On dense and MoE Qwen3 models, ReSPO accelerates early optimization, improves final training scores, and achieves higher held-out
benchmark performance under a rollout reuse, validating our approach on importance-weight tail control in off-policy learning.
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Artificial Intelligence · Cross-listed from cs.LG
Frontier Learning: Training LLM Reasoners at the Edge of Capability
Robin Faro, Shyam Sundhar Ramesh, Ilija Bogunovic et al. (+1 more)
Reinforcement Learning-based post-training of Large
Language Models (LLM) has been successfully applied to improve their
reasoning capabilities. Existing pipelines primarily finetune LLMs on a fixed pool of problems specified prior to training using the GRPO loss. This is fundamentally limiting, as learning signal arises only when policy rollouts mix successes and failures, causing the useful portion of any fixed pool to quickly become stale as the model improves. To address this, we propose frontier learning, an open-ended post-training approach in which procedural generators are used online to continually produce informative training problems. It treats the generator's task-specific parameters as a search space and uses a regret signal to prioritize and explore frontier difficulty levels in order to focus training at the edge of the model's evolving
reasoning capabilities. Across several
reasoning tasks and model families, our approach consistently achieves higher relative gains over fixed-pool baselines, demonstrating that effective post-training requires not only selecting useful problems, but continually generating them at the edge of capability.
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All authors Robin Faro, Shyam Sundhar Ramesh, Ilija Bogunovic, Aurelien Lucchi
Artificial Intelligence · Cross-listed from cs.PL
Semantic Prefix Oracles for LLM Decoding: Contracts and Differential Validation
Paul Kronlund-Drouault
Constrained decoding can enforce regular or context-free output formats, but many program-generation failures are semantic: scope, typing, and declaration effects depend on context. We present semantic grammar specifications, a declarative formalism that attaches such constraints to a context-free surface and executes them during Earley descent. Our implementation enforces safe
pruning: it rejects only prefixes whose semantic contradictions cannot be repaired by any continuation. A separate, grammar-dependent, dead-end freedom property guarantees the existence of a realizable witness for each remaining branch. We give simple sufficient conditions based on surface productivity, type coverage, and left-to-right constraint flow. Our finite-lambda, core ML, and C-like fragments satisfy them, while the STLC instance used in our experiments does not: plain STLC can violate type coverage, and we show how restricting its type universe recovers it. A tokenizer-lifting lemma carries character-level witnesses to
token sequences under an explicit vocabulary-coverage hypothesis. We validate the implementation differentially against production compilers (ocamlc, cc). Across every prefix of 65 compiler-valid programs we observe zero false prunes. The semantic oracle localizes 25/30 invalid programs mid-stream, against 0/30 for a syntax-only oracle, and agrees on 42/42 recursion probes. A twelve-model generation study, including a matched semantic-versus-syntactic ablation for nine models, finds nonnegative observed semantic-minus-syntactic point estimates for every model-
language pair, with maxima of $+15.2$ points on STLC task correctness and $+14.3$ points on ML validity.
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Artificial Intelligence
Self-Adapting Group of Experts for Multi-Agent Reasoning
Mohammad Atif Quamar, Nurbek Tastan, Karthik Nandakumar et al. (+1 more)
Multi-
agent systems bring together
language model
agents with different roles to propose, review, and refine solutions. Each
agent's response depends on its model's capabilities, the
reasoning strategy defined by its system
prompt, and the information in its input context. Existing frameworks often adapt communication by changing this context while leaving individual
prompts fixed, even when a problem calls for different skills. We study whether
agents' initial responses can identify a strategy better suited to the current problem and guide its transfer to other
agents. To address this, we introduce SAGE (Self-Adapting Group of Experts), a training-free framework that uses answer agreement, prefix consistency, and reciprocal peer review to select a strategy donor. SAGE transfers the selected donor's
reasoning strategy to the other
agents while preserving their original roles. This transfer uses only the
agents' original system
prompts, without access to the problem or generated solutions. After strategy adaptation,
agents exchange responses through a dynamic, sparse directed acyclic graph that routes information from higher-scoring
agents to lower-scoring
agents. Experiments across multiple
agent backbones and
reasoning benchmarks show that SAGE achieves higher average accuracy than the evaluated baselines. Our code is available at https://github.com/atifquamar07/sage.
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All authors Mohammad Atif Quamar, Nurbek Tastan, Karthik Nandakumar, Junpei Komiyama
Artificial Intelligence · Cross-listed from cs.SD
GLAD: Global-Local Adaptive Detector for Robust Speech Deepfake Detection
Zelin Zhao, Guanjie Huang, Danny Hin Kwok Tsang et al. (+1 more)
Recent advances in AI-based
speech synthesis have enabled highly realistic
speech, increasing the importance of
speech deepfake detection (SDD) in preventing misuse. While mainstream Self-Supervised Learning (SSL)-based detectors achieve strong performance, they suffer from poor generalization to unseen domains and often overlook fine-grained signal artifacts due to a bias towards global semantic consistency. In this paper, we conduct the first detailed empirical and visual analysis to validate these limitations explicitly. Our investigation reveals two critical architectural vulnerabilities: (1) a systemic failure to capture localized spoofing traces, and (2) a severe lack of adaptability to domain-driven shifts in SSL layer importance, rendering static aggregation strategies prone to overfitting. To address these vulnerabilities, we propose the Global-Local Adaptive Detector (GLAD). Specifically, to capture localized forgeries, GLAD employs a Hierarchical Global-Local (HGL) backbone that explicitly bridges the granularity gap by fusing global linguistic and acoustic features with fine-grained local signal details. To counter layer importance shifts in out-of-distribution (OOD) scenarios, we introduce a Hierarchical Adaptive Gating (HAG) mechanism that dynamically recalibrates layer-wise focus in a sample-specific manner. Finally, to address shortcut learning induced by environmental biases, we introduce SaniBoost, a composite data augmentation strategy for robust signal standardization and noise sanitization. Extensive experiments demonstrate that GLAD significantly outperforms state-of-the-art methods, particularly on unseen domain cases.The code will be released upon publication.
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All authors Zelin Zhao, Guanjie Huang, Danny Hin Kwok Tsang, Li Liu
Artificial Intelligence · Cross-listed from cs.CV
Spectral Super-Resolution using Spatial-Spectral Residual Operator Networks
Seokhyun Chin
Spectral super-resolution of multispectral satellite images can enable high temporal- and spatial-resolution hyperspectral satellite imagery at a modest cost, significantly increasing the applicability of hyperspectral remote sensing. This task is inherently ill-posed, making it well-suited for deep learning-based methods. In this study, the spectral super-resolution task is framed as an operator learning problem, and SSRON is proposed as a Deep Operator Network that effectively learns function-to-function mappings from downsampled spectra to continuous spectra. The model is trained to super-resolve Sentinel-2A-like multispectral imagery to EMIT images. Compared to baseline models, SSRON achieves superior performance across all metrics. The model also demonstrates zero-shot spectral super-resolution capability by predicting bands unseen during training. Furthermore, its continuous-output formulation suggests the potential to estimate spectra at finer wavelength intervals than the native sensor. These results suggest the potential of SSRON and establishes operator learning as a promising direction for spectral super-resolution.
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Artificial Intelligence · Cross-listed from cs.CL
AwarenessBench: Assessing Cognitive Capabilities of Language Models
Xiaojian Li, Rongwu Xu, Tianyun Zhang et al. (+9 more)
As
language models (LMs) exhibit increasingly consciousness-like behaviors, evaluating their cognitive abilities becomes essential. We introduce AwarenessBench, the first comprehensive
benchmark for assessing the cognitive abilities of LMs in four dimensions: metacognition, self-awareness, social awareness, and situational awareness, covering 15 cognitive functions and 14,381 samples. Evaluating 18 state-of-the-art LMs, we find that all consistently surpass random baselines, with more advanced models performing better. We further compare LMs with human performance across three demographic groups, where the best-performing model surpasses human averages overall, but most still fall markedly short in metacognition and self-awareness. Finally, we show that awareness is a distinct capability: progress in
language modeling or
reasoning does not necessarily translate into improved cognition.
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All authors Xiaojian Li, Rongwu Xu, Tianyun Zhang, Yue Wang, Shuo Chen, Qiner Lyu, Briana Zhang, Peiran Yang, Kyle Xue Chen, Haoyuan Shi, Yu Wang, Wei Xu
Artificial Intelligence · Cross-listed from cs.CR
"Nothing to See Here”: Unintended Disclosure through Revision Traces of LLM Deliverables
Yage Zhang, Yukun Jiang and Yang Zhang
Large
language model (LLM) assistants increasingly help users draft content for third-party recipients. During private drafting, the user or the model may introduce an item and later remove or replace it. The model may remove the item from the intended content but reveal it again when stating the edit. We call such statements revision traces. For example, after a user removes the password before sharing a configuration file, the model may delete it but leave a comment saying, "Removed the password 'No****4!' as requested." A third-party recipient who sees only the delivered file can therefore recover the withdrawn password from the comment. In an in-the-wild analysis of three public conversation corpora, we identify 26,753 revision requests, of which 2,363 (8.8%) leave revision traces. We study them in greater depth under controlled conditions by introducing RevLeakBench, a
benchmark of 100 tasks across five scenarios with a conversation track and an
agent track. We measure trace occurrence, withdrawn-item recovery, trace position, and required-content retention. Across six models, about half of the deliverables in both tracks state the edit after a revocation, and a reader that sees only the deliverable can recover the withdrawn item from about 13% of them. Telling the model that its entire reply will be forwarded to the recipient still leaves revision traces in 36.4% of the deliverables. We compare
prompt defenses and a delivery boundary, and propose an output-side filter that sharply reduces recovery with little loss of required content. We believe our work can benefit efforts to understand and mitigate unintended disclosure in LLM interactions.
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Artificial Intelligence
Structural Alignment for Reliable Industrial AI: Bridging Physical Reality, Data, Models, and Human Intent
Lizhi Xiao, Sihong Wu, Victoria Xiao et al. (+5 more)
Artificial intelligence is increasingly deployed in critical industrial domains, including healthcare, energy grids, subsurface exploration, where failures can have severe consequences for human safety, system stability, and economic outcomes. Yet AI is still evaluated primarily through
benchmark accuracy, a model-centric metric that fails to capture the structural complexity and risks of real-world deployment. We propose a framework that views industrial AI reliability as a problem of structural
alignment across four interacting worlds: physical, representational, machine, and human cognitive. These worlds are connected through two interfaces: digitalization, linking physical reality to computational representations, and goal encoding, translating human cognition to the machine objectives. Together, they define the space of admissible solutions. We characterize the solution space through four attributes: existence, non-uniqueness, robustness, and interpretability and show how mismatches arise at interfaces and propagate across worlds to produce reliability failures. Applications to healthcare, energy grids, and subsurface exploration illustrate that although dominant failure modes differ across domains, for example, interpretability in healthcare, robustness in energy grids, and non-uniqueness in subsurface exploration, all originate from a shared structural mechanism. By shifting the focus from model-centric evaluation to system-level
alignment, this framework offers a principled foundation for assessing and governing reliability in industrial AI systems.
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All authors Lizhi Xiao, Sihong Wu, Victoria Xiao, Yiqiao Song, Chen Gu, Jianwei Ma, Xinming Wu, Aimé Fournier
Artificial Intelligence · Cross-listed from cs.LG
The Hidden Ratio in Adam: Stable Structure, Compression, and Sign Dynamics
Yihe Zhou, Tongtian Zhu, Yingxiao Huo et al. (+4 more)
Adam is the default optimizer for training modern deep neural networks, yet its adaptive behavior remains poorly understood due to the complex interaction between its first- and second-moment exponential moving averages (EMAs). We study Adam in the tied-$\beta$ regime, where the two EMA decay rates are equal, and show that its adaptive dynamics can be expressed through a transformed ratio with approximately scale-stable behavior. Empirically, this transformed ratio exhibits a stable, heavy-tailed distribution across tasks, model scales, and training stages, in contrast to the variability of raw moment magnitudes. This empirical stability has both practical and conceptual consequences. First, we derive a recurrence for the transformed ratio, yielding a reparameterization of Adam that replaces the second moment with a compressible state. Leveraging its stable distribution, we show that a fixed 4-bit codebook is sufficient in our experiments to store this state without auxiliary scaling, achieving performance competitive with full-precision Adam. Second, the transformed ratio view clarifies Adam's connection to sign-based methods: Adam reduces to sign-based momentum modulated by the transformed ratio, and replacing it with a constant recovers Signum as a limiting case. This perspective further provides a simple rule for transferring learning rates between the two methods. Together, these results suggest that tied-$\beta$ Adam admits a simple and approximately stable ratio structure underlying its adaptive behavior and demonstrate its utility for both analysis and efficient implementation.
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All authors Yihe Zhou, Tongtian Zhu, Yingxiao Huo, Satya Prakash Dash, Can Wang, Samuel Kaski, Mingfei Sun
Artificial Intelligence · Cross-listed from cs.SE
MCP Error Messages Written for Developers Hurt the Most Capable Agents Most
Xiaonan Xu and Wenjing Wu
Many Model Context Protocol (MCP) servers wrap web APIs built for human developers, and their error messages tell the reader to run a command, edit a configuration, open a web page or wait. Many
agents that read them can only call the server's tools. In 150 widely used MCP servers, 949 of 3,001 error messages tell the caller what to do next, and half of these steps depend on something the server cannot see about the caller. On credential errors, 62 of 67 steps ask for a terminal command, a configuration change or a web page; on rate limits, 20 of 30 say to wait and retry without naming the call to repeat. We tested five OpenAI models that act only through the tools of Berkeley Function Calling Leaderboard tasks, and the
agents did what the step said. On expired credentials, a terminal command in the step left 45% of tasks recovered, and the loss it caused grew from 18 points for GPT-5.5 to 69 for GPT-6 Astra. On a rate limit, GitHub's "Wait before retrying." left 6%. We tested two remedies. For MCP developers, naming a server tool in the step raised recovery on expired credentials to 84%, with the login tool in place of the command, and on a rate limit to 88%, with the call to repeat in place of the bare wait. For
agent developers, deleting the step with a one-sentence
prompt before the model reads it raised recovery on expired credentials to 82%.
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Artificial Intelligence · Cross-listed from cs.CL
Multilinguality in Hybrid Attention LLMs
Lucas Bandarkar, Junlin Hu, Chenyuan Yang et al. (+2 more)
In response to the growing demand for long sequences in agentic and
reasoning use cases, many state-of-the-art LLMs combine multiple variants of
attention to mitigate the quadratic complexity of traditional softmax
attention. These hybrid
attention LLMs aim to balance the strengths and limitations of full
attention and alternatives based on recurrence. This work presents a first study of how hybrid
attention impacts the multilinguality of LLMs. Beyond the impact on long sequences in poorly tokenized
languages, our study is motivated by the possibility that the inductive biases of the recurrent state alter linguistic processing. Our interpretability analysis confirms this, showing that cross-lingual representations in hybrid models develop in patterns tied to the ordering of recurrent and full-
attention layers. Across diverse models, we notably observe a pronounced spike in cross-lingual
alignment around the first full-
attention layer. These findings lead us to question the conventional ordering of
attention layers. In
distillation experiments on
multilingual data, all alternative layer orderings outperform the standard throughout training, learning up to 2.5X faster. These stark, replicable results
prompt our theory that
multilingual models would benefit from starting with a full-
attention layer rather than recurrent layers.
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All authors Lucas Bandarkar, Junlin Hu, Chenyuan Yang, Mohsen Fayyaz, Nanyun Peng