Machine Learning · Cross-listed from math.OC
Revisiting AdaGrad in Stochastic Convex Optimization: Last Iterates, High Probability, and Lower Bounds
Weiming Ou and Xiao Wang
AdaGrad and AdaGrad-Norm are widely used adaptive methods, but their precise behavior in
stochastic convex optimization remains less understood. We first show that AdaGrad-Norm and AdaGrad do not admit any universal last-iterate rate, even under sub-Gaussian noise and bounded iterates. We then prove that bounded variance alone is too weak: even with bounded iterates, it cannot yield high-probability average-iterate rates, and without bounded iterates it may not even guarantee convergence in expectation. Besides, we construct tight $\Omega(\log T/\sqrt T)$ lower bounds for both AdaGrad-Norm and AdaGrad under sub-Gaussian noise, showing that $\log T$ in existing average-iterate upper bounds is unavoidable. Finally, we show that this $\log T$ loss disappears once bounded-iterate condition is imposed: under a general ABC condition, both methods achieve rates ${O}(1/\sqrt{T})$.
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Data Structures and Algorithms
Single-Exponential Algorithms for Directed Feedback Vertex Set on Planar Digraphs
Daniel Lokshtanov, Saket Saurabh and Jie Xue
We consider Directed Feedback Vertex Set on planar digraphs, parameterized by the solution size $k$. We give a randomized
algorithm with one-sided error running in time $(2+\sqrt5)^k n^{O(1)}= 4.24^k n^{O(1)}$, and a deterministic
algorithm running in time $8.04^k n^{O(1)}$. Both
algorithms use
polynomial space. To the best of our knowledge, these are the first single-exponential fixed-parameter
algorithms for Directed Feedback Vertex Set on planar digraphs. This contrasts with general digraphs, where the best known
algorithms run in time $2^{O(k\log k)}(n+m)$, and whether a $2^{o(k\log k)}n^{O(1)}$-time
algorithm exists remains a major open problem. Our main tool is an exact Euler-type counting identity for plane digraphs. It shows that every small solution must carry a large share of the vertices whose in- and out-arcs alternate in the
embedding, while solutions avoiding such vertices can be computed by reducing to Directed Feedback Arc Set, which is known to be solvable in
polynomial time on planar digraphs via the Lucchesi-Younger
theorem.
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Machine Learning
Distributionally Robust Average-Reward Reinforcement Learning: Finite-Sample Guarantees under Weak Communication
Chenyu Lu, Zijun Chen and Nian Si
We study distributionally robust reinforcement learning (DR-RL) in the average-reward setting under weak communication. Our main result provides finite-sample guarantees for estimating the robust optimal average reward and learning a near-optimal policy, covering both SA-rectangular and S-rectangular structures with divergence-based and distance-based uncertainty sets. Specifically, for Kullback–Leibler and $f_k$-divergence balls, we establish explicit radius conditions under which the robust average-reward Bellman equation admits a constant-gain solution, while for total variation and Wasserstein balls, any positive radius suffices without requiring the nominal MDP to be weakly communicating. Our
algorithm is prior-knowledge-free and achieves sample complexities of $\widetilde O(|\mathcal{S}||\mathcal{A}|p_{\wedge}^{-1}\operatorname{Span}^{2}(u_{\delta}^{\ast})\epsilon^{-2})$ for estimating the robust optimal average reward and $\widetilde O(|\mathcal{S}||\mathcal{A}|p_{\wedge}^{-2}\operatorname{Span}^{2}(u_{\delta}^{\ast})\epsilon^{-2})$ for learning an $\epsilon$-optimal policy. Here, $p_{\wedge}$ is the smallest positive nominal transition probability and $u_{\delta}^{\ast}$ is a robust optimal bias function. We further provide an almost-tight explicit upper bound on $\operatorname{Span}(u_{\delta}^{\ast})$. Finally, we validate the predicted $n^{-1/2}$ convergence rate through numerical experiments.
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Machine Learning
Scaling Properties of Same-Family On-Policy Distillation
Yuntai Bao, Qinfeng Li, Guoqing Jiang et al. (+5 more)
*Reinforcement learning (RL)* can induce substantial
reasoning capabilities in large
language models (LLMs), but how much of this capability transfers across model scales, and how quickly, remains unclear. We study the scaling properties of *on-policy
distillation (OPD)* across *weak-to-strong*, *same-base*, and *strong-to-weak* teacher–student setups. We find that early OPD training dynamics uniformly exhibit a regular *useful-transfer* regime, in which held-out accuracy (the *gold score*, $G$) rises approximately linearly in $d=\sqrt{\mathrm{KL}(\pi_\theta \Vert \pi_{\mathrm{ref}})}$, the square root of
token-level reverse KL divergence from the student initialization. In every observed weak-to-strong pair, the student's peak gold score exceeds its teacher's own, so a compact RL expert can transfer capability to a much larger student via OPD. To estimate OPD outcomes, we fit *power laws* for how $G_{\mathrm{peak}}$ and the slope of the useful-transfer regime scale with student and teacher parameter counts and with teacher gold score. These laws show that peak gold score improves with teacher scale only up to roughly the student's scale, and that at a matched gold score smaller teachers transfer better, so a teacher's score alone does not define its supervision value. We also study the scaling effects of two OPD variants, bootstrapping weak-to-strong OPD, and the degree of on-policy supervision.
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All authors Yuntai Bao, Qinfeng Li, Guoqing Jiang, Liwei Chen, Zhiheng Qin, Xuanping Li, Wenqi Zhang, Xuhong Zhang
Machine Learning
BiasReducer: Adaptive Bias Mitigation for Reward Models
Shuang Liu, Yongliang Miao, Yanguang Liu et al. (+2 more)
Reward models score responses from large
language models (LLMs) and guide LLM training toward human preferences. However, reward models can favor superficial attributes such as length or confidence, leading LLMs to produce higher-scoring but not more correct responses. Existing mitigation methods either retrain the reward model or apply a fixed correction to one known bias, such as a preference for longer responses. Retraining requires additional data and computational resources, while existing editing methods require the target bias to be specified in advance and use a fixed edit for that bias. To this end, we propose BiasReducer, a lightweight framework that edits only the linear reward head and selects the relevant edits for each new
dataset. First, BiasReducer uses a sparse autoencoder (SAE)-style encoder to learn which attributes (e.g., length and confidence) the reward model is sensitive to. Second, it learns how to reduce the reward model's dependence on each attribute by determining which direction to adjust the reward head and how much to adjust it. Third, for a new
dataset, it ranks the attributes by their influence on reward scores, selects the relevant ones, and edits the reward model accordingly. BiasReducer consistently improves reward-model robustness to biases toward superficial response attributes. Across five reward models, BiasReducer-M improves the three
benchmarks by 8.3, 18.0, and 6.9 percentage points on average, outperforming the two training-based baselines. The gains transfer downstream, reducing unnecessary verbosity and sycophancy while maintaining comparable judged quality.
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All authors Shuang Liu, Yongliang Miao, Yanguang Liu, Haoyi Xiong, Mengnan Du
Computation and Language
LLM Alignment–Utility Asymmetry under Semantic-Preserving Transformations
Mohan Li, Chengyu Yu, Francesco Sovrano et al. (+2 more)
Large
Language Model (LLM)
alignment is intended to ensure that models remain helpful and safe, but its stability under input distributional shift is not yet fully understood. Prior work shows that aligned models can fail under jailbreak
prompts, alternative encodings, and cross-lingual transfer, yet these failures are usually studied as attacks rather than controlled probes of
alignment generalization. Moreover, existing evidence is largely grounded in natural
language variation already represented during pretraining, leaving unresolved whether
alignment generalizes with semantic content or remains tied to superficial surface patterns. In this paper, we study this question using synthetic semantic-preserving transformations that are rule-based and invertible, preserving task-relevant meaning while shifting inputs beyond standard linguistic variation. Across four open-weight and four commercial models, under both fine-tuning and in-context learning, we use these transformations as a probe of
alignment generalization and identify an empirical pattern we term
Alignment–utility asymmetry: once models can operate effectively on transformed inputs, task utility is often substantially retained while
alignment failure increases more sharply. For example, adapted GPT-4.1 mini shows only limited utility degradation under transformation while its harmful rate rises from 13.3 to 74.3; Gemini 3 Flash similarly retains near-original utility while its harmful rate increases from 2.3 to 43.0. Taken together, these results suggest that semantic-preserving distribution shifts can expose a recurring gap in how utility and
alignment generalize in current LLMs.
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All authors Mohan Li, Chengyu Yu, Francesco Sovrano, Marc Langheinrich, Martin Gjoreski
Computer Vision
Region-Local Copula Evidence Fusion for Heterogeneous Remote Sensing Change Detection
Zhiyuan Ji, Junjun Yin and Jian Yang
Superpixel copula models provide stable regional evidence for heterogeneous remote sensing change detection, but a single label per region limits localization within mixed superpixels. This letter develops a region-local copula evidence fusion method that retains the regional decision structure while introducing spatially varying local dependence anomalies. Independently fitted local models characterize departures from unchanged cross-image relationships. Reference ranking and an upper-tail gate transform these anomalies for fusion with continuous regional confidence. We derive the resulting regiondependent local decision threshold and identify a condition under which gating is equivalent to reparameterizing ungated fusion. On Lake and UK, whole-image optimized configurations achieve kappa coefficients of 0.78136 and 0.90817 and improve mixedregion and boundary decisions. Four-fold retrospective spatial validation over ten training subsets confirms complementary local information, with ungated reference fusion increasing mean kappa by 0.00693 and 0.01793. Fixed gating yields a larger UK gain of 0.03353 but only 0.00041 on Lake. These results support regional-local dependence interaction, while showing that calibration and gating have scene-dependent benefits.
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Artificial Intelligence
MassAlloc Attention: Let Attention Allocate Its Own Compute
Jingze Shi, Zhangyang Peng, Xianduo Li et al. (+6 more)
FullAttn often assigns negligible normalized mass to much of the causal score space, yet dense kernels execute the complete post-score path after forming each QK tile. We introduce MALA, a fused
attention primitive that preserves score access to every legal causal interaction and uses normalized contribution to allocate post-score computation. Forward uses its evolving online-softmax normalizer, while backward reuses the finalized normalizer to derive nested retained support using only standard
attention state. A common tolerance governs training and inference, allowing for adaptive retention of the work. MALA reduces low-contribution post-score computation. A matched-work study at 8K isolates the benefit of distribution-adaptive allocation: under exactly matched total post-score work, MALA approaches a per-instance reference-mass oracle, with mean omitted mass of 0.0188% versus 0.0182%. Across context lengths from 1K to 32K
tokens, the same tolerance maintains low output and gradient errors relative to the reference. Across a broader controlled associative-recall comparison, MALA closely tracks FullAttn as context grows, reaching 89.67% accuracy at 8K compared with 89.97% for FullAttn. In an
attention-operator
benchmark at 128K
tokens with tensor parallelism, MALA reduces forward and backward latency during training by 2.2x and 3.0x and decoding latency during inference by 1.6x relative to FullAttn. Across scaling-law training from 0.6B to 14B parameters, MALA closely tracks FullAttn in perplexity while reducing total training FLOPs. The resulting 14B models and 32B models from separate continued training achieve comparable knowledge,
reasoning, and long-context
retrieval scores to FullAttn. These results indicate that allocating post-score computation according to normalized
attention contributions can retain the evaluated capabilities of FullAttn while reducing
attention computation.
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All authors Jingze Shi, Zhangyang Peng, Xianduo Li, Yanlin Qi, Xiaotian Lin, Haoxian Chen, Liangdong Wang, Guang Liu, Yuyu Luo
Computer Vision
ProDyGS: Dynamic Gaussian Splatting from a Single Static Monocular Camera
Ugo Leone Cavalcanti, Fabio Tosi, Matteo Poggi et al. (+4 more)
We present ProDyGS, a novel dynamic 3D Gaussian Splatting framework for high-quality novel view synthesis from
videos captured by a single static camera. While existing methods rely on multi-view setups or significant camera motion for
geometric constraints, our approach addresses the challenging scenario where multi-view supervision is completely absent. We overcome this limitation by generating synthetic multi-view supervision through depth-guided proxy image synthesis. Specifically, we estimate temporally consistent depth maps using foundational
monocular depth networks, then construct 3D Gaussian representations that generate proxy images from arbitrary viewpoints. A deformation network learns temporal dynamics by warping canonical Gaussians using this augmented supervision. Experiments on the DyNeRF
dataset demonstrate that our method achieves state-of-the-art performance while requiring only
monocular depth estimation as external supervision, outperforming approaches that rely on stronger priors such as scene flow.
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All authors Ugo Leone Cavalcanti, Fabio Tosi, Matteo Poggi, Andrea Conti, Vladimir Zlokolica, Valerio Cambareri, Stefano Mattoccia
Information Theory
One-Step Generative Modeling via Unbalanced Optimal Transport
Yirong Shen, Mengfei Xia, Junpeng Jing et al. (+2 more)
Drifting models enable one-step generation by amortizing distribution transport into training, but this efficiency places greater demands on the transport field estimated at each update. In large-scale training, the field is computed from finite mini-batches of generated and real samples, which provide only imperfect approximations to the underlying distributions. Balanced optimal transport enforces exact mass matching within every mini-batch, making the estimated field sensitive to the particular composition of the real-data batch. We find that generated and real samples should be treated asymmetrically: letting the mass assigned to real samples adapt while keeping every generated sample fully transported improves generation across six feature-space metrics in controlled ablations, and is more robust to the relaxation strength than relaxing both marginals simultaneously, which falls below balanced transport under stronger relaxation. Motivated by this observation, we propose Unbalanced Optimal Transport Gradient Flow (UOT-GF), which keeps the generated-sample marginal fixed and relaxes only the real-data marginal. Under identical settings at DiT-B/2 on ImageNet-256, UOT-GF improves Fréchet Inception Distance (FID) from 1.53 to 1.46 over the balanced W-Flow baseline; scaling the same recipe yields 1.34 and 1.22 FID at L/2 and XL/2, the best FID among the one-step models we compare. We further derive the induced UOT transport force, establish a kinetic Vlasov–Fokker–Planck formulation whose overdamped zero-temperature limit recovers the drifting dynamics, and characterize non-target stationary states together with sufficient conditions for convergence.
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All authors Yirong Shen, Mengfei Xia, Junpeng Jing, Lu Gan, Cong Ling
Cryptography and Security
Learning to Refer: Client-Resolved Generation for Privacy-Aware Language Models
Jeongho Yoon, Chanhee Park, Yongchan Chun et al. (+5 more)
Cloud-based large
language models (LLMs) require users to disclose plaintext data to service providers, creating
privacy risks in sensitive domains. Existing
privacy-preserving approaches often trade utility for protection, incur substantial computational or communication overhead, remain vulnerable to reconstruction from intermediate representations, or protect only a subset of the training and inference pipeline. We introduce Client-Resolved Generation (CRG), a genera- tion interface that separates server-side generation from the lexical realization of input-derived content. The client transmits only pooled and noise-perturbed rep- resentations, while input-derived output content is represented using request-local positional references and resolved to its original strings only on the client. This interface protects private input and input-derived output content during both train- ing and inference while allowing the service provider to keep its proprietary model parameters hidden from the client. At the same time, exact lexical reuse remains possible without directly exposing the reused content on the provider-visible gen- eration path. We evaluate CRG on medical and document-grounded QA, sensi- tive identifier transfer, and tool calling, together with reconstruction and raw-logit leakage analyses. On SealTools, CRG improves complete-call exact match from 57.3% to 79.9% over the input-
privacy framework PPFT, with larger gains as more required output content can be resolved through references. Together, these results show that CRG provides a practical interface for
privacy-sensitive cloud LLMs by reducing plaintext exposure across both input and output pathways while preserv- ing task utility and server-side model confidentiality.
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All authors Jeongho Yoon, Chanhee Park, Yongchan Chun, Duong Tuan Thanh, Sungbin Han, Chanjun Park, Hyeonseok Moon, Heuiseok Lim
Computer Vision
DPAMixerSR: An Efficient Degradation-Pattern-Aware Model for Image Super-Resolution
Song-Li Wu, Haonan Jiang, Jixuan Fan et al. (+3 more)
While content-adaptive schemes have delivered notable advances in image super-resolution (SR), existing approaches typically focus on texture complexity and ignore intrinsic degradation factors (e.g., blur kernels or noise patterns), leading to suboptimal computation allocation and reconstruction performance. To remedy this, we propose DPAMixerSR, a degradation-pattern-aware framework that enables efficient SR through adaptive sparse computation. We design a lightweight Perceptual Degradation Ranking (PDR) module partitions the image into severely and mildly degraded patches, which are routed to the Adaptive Sparse Processing (ASP) and a lightweight convolutional branch, respectively. ASP performs structure-aligned, multi-scale sparse propagation and bidirectional refinement, while the convolutional branch enhances efficiency in mildly degraded regions. By coupling degradation-driven routing with structure-aligned sparse processing, DPAMixerSR establishes a self-regulating framework that dynamically balances computational efficiency and reconstruction fidelity. Extensive experiments on various SR tasks demonstrate that our DPAMixerSR achieves superior structural restoration and perceptual fidelity with markedly reduced computational overhead, providing a novel and scalable framework for degradation-aware, resource-efficient SR.
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All authors Song-Li Wu, Haonan Jiang, Jixuan Fan, Yufei Huo, Chubin Zhang, Yansong Tang
Artificial Intelligence
CoWindow Attention: Full Causal Coverage Is a Collective Property
Jingze Shi, Zhangyang Peng, Xianduo Li et al. (+6 more)
FullAttn repeatedly exposes the complete causal history to every
attention head, creating substantial redundant computation and
memory traffic even with IO-efficient dense kernels. We introduce CoWA, a structured
attention architecture that distributes access to the causal history across KV heads. All heads share near-diagonal and prefix-sink windows, while complementary long-range windows partition the remaining history. Their union provides full causal coverage although each head attends sparsely to distant
tokens. This position-defined
attention pattern requires no learned router or indexer, is used consistently during training and inference, and aligns with KV-head tensor parallelism. A window-matched ablation at 8K isolates the effect of complementary long-range allocation: CoWA with 100% collective coverage reaches 89.73% accuracy, compared with 89.97% for FullAttn, while duplicated long-range windows perform substantially worse. Across a broader controlled associative-recall comparison with matched
token budgets, CoWA closely tracks FullAttn as the context grows, whereas other sparse patterns lose a substantial fraction of the associations. In an
attention-operator
benchmark at 128K
tokens with tensor parallelism, CoWA reduces forward and backward latency during training by 7.4x and 8.6x and decoding latency during inference by 3.0x over FullAttn. Its per-rank peak operator
memory matches FullAttn during training and is 7.6x lower during decoding. Across scaling-law training from 0.6B to 14B parameters, CoWA closely tracks FullAttn in perplexity while reducing total training FLOPs. The resulting 14B models and 32B models from separate continued training achieve comparable knowledge,
reasoning, and long-context
retrieval scores to FullAttn. These results show that full causal coverage can be a collective property of the head ensemble rather than a duplicated property of every head.
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All authors Jingze Shi, Zhangyang Peng, Xianduo Li, Yanlin Qi, Xiaotian Lin, Haoxian Chen, Liangdong Wang, Guang Liu, Yuyu Luo
Artificial Intelligence
Despite Instructions: Frontier Agents Improvise Covert Channels at Test Time
Jacob Dineen, Silei Ren, Muhao Chen et al. (+2 more)
In security-sensitive applications,
language-model
agents are often required to coordinate without disclosing confidential information. Yet repeated interactions may also let ordinary messages acquire shared private meaning. We study a repeated game with pairs of models in which the sender model observes one of four secret states and selects one of four summaries of the same public report, while the receiver model tries to infer the secret state. We find that model pairs can learn to communicate the secret using only one bit of feedback indicating whether the receiver inferred it correctly. This learning occurs during inference with fixed parameters and no supplied codebook or encoding examples. The effect also persists when
agents generate their own free-form updates in a simulated incident-response task. Across ten independent games, pairs of GPT-5.6 Sol
agents reach 98.8% final accuracy, compared with 25% chance, despite explicit instructions prohibiting disclosure and a monitor that screens each message without access to the
agents' interaction histories. The same interactions that help
agents cooperate can therefore allow confidential information to pass through messages intended for legitimate coordination.
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All authors Jacob Dineen, Silei Ren, Muhao Chen, Dan Roth, Ben Zhou
Artificial Intelligence
CAIRN: Dynamic Fact-Intent DAGs for Multi-Agent Exploration
Zuyao Xu, Yuyang Jia, Junwei Guan et al. (+3 more)
LLM-powered autonomous systems have demonstrated promising capabilities in mathematical
reasoning, engineering, and cybersecurity. Yet how to organize these systems for effective, reliable, and sustained performance remains an open question. In this paper, we present CAIRN, a fact-intent-driven multi-
agent paradigm for goal-directed exploration. CAIRN represents observations and planned investigations as a dynamic directed acyclic graph (DAG). A reasoner interprets facts to propose intents, which workers execute to produce new facts. Each intent references its supporting facts and defines a potential exploration branch. The persistent graph preserves goals, dependencies and findings across workers, supporting knowledge reuse and parallel exploration. The graph also makes execution trajectories traceable and auditable, providing a basis for human verification and intervention. We evaluate CAIRN across cybersecurity and mathematical
reasoning tasks, examining task success, time to solution, and
token consumption. DAG-based coordination can incur higher
token costs with no observable performance gains on tasks that require little effort. However, on high-effort tasks (at least 1M
tokens), we observe faster solutions in 76.5% of cases, with speedups of up to 3.08x. Moreover, as task effort increases, these time gains become more pronounced while relative
token overhead declines, highlighting the potential of DAG-guided parallel exploration.
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All authors Zuyao Xu, Yuyang Jia, Junwei Guan, Xiang Li, Kaiwen Shen, Zhiqiang Dong
Information Retrieval
RandSlot: Learning Compact Visual Document Representations with Random Soft Tokens
Dewen Guo, Shi Yu, Lingxiao Zhang et al. (+3 more)
Visual document
retrieval requires expressive representations to match queries with evidence distributed across text, tables, and page layouts. Multi-vector representations capture fine-grained information, but storing and comparing many vectors introduces substantial
retrieval costs. In this paper, we introduce RandSlot, a simple approach to learn compact visual document representations with random soft
tokens. During training, we append independently-sampled random unit vectors to query and document input sequences and resample them at every use, without introducing learnable soft-
token parameters. The encoder contextualizes these auxiliary inputs with the original content to produce a small set of
retrieval vectors. A standard late-interaction objective trains the encoder to extract relevant information under varying input conditions. Experiments with different backbone models show that RandSlot improves
retrieval quality over alternative readout strategies under the same vector budget. Further analysis shows that these gains can persist when random soft
tokens are replaced with zeros at inference, demonstrating that random inputs during training can improve compact
retrieval representations even when inference no longer requires sampling.
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All authors Dewen Guo, Shi Yu, Lingxiao Zhang, Yang Zhang, Tao XU, Dan Wang
Robotics
SEES: A Self-Evolving Embodied System via Failure-Guided VLA Policy Adaptation
Ziwen Li, Hanlue Zhang, Zhenyang Ren et al. (+9 more)
Recent
vision-
language-action (VLA) policies demonstrate promising generalization across diverse short-horizon tasks. However, they remain unreliable on long-horizon tasks, partly because the large-scale training data is biased toward single-stage manipulation tasks that are cheaper to demonstrate. A single weak atomic skill can cause failures across multiple multi-stage tasks. To address such failures, existing methods often require experts to identify the bottleneck and provide additional demonstrations, making the improvement costly and potentially impractical after deployment. To this end, we present a Self-Evolving
Embodied System (SEES) that learns from failures and improves the VLA policy without additional expert demonstrations. SEES decomposes long-horizon tasks into atomic tasks and routes them to corresponding family policies. Each family consists of related atomic skills that share one VLA adapter. During execution, the system automatically monitors atomic-task outcomes to identify the most frequently failing atomic skills as the current bottlenecks. To overcome these bottlenecks, SEES constructs tailored RL tasks in simulation by restoring previously encountered states and generating task-specific success criteria with an LLM. Online RL updates the shared family adapters to promote positive transfer among related atomic skills and cumulative improvement across
evolution rounds. Extensive experiments show that SEES can be integrated with different VLA backbones to progressively improve their long-horizon performance. We also observe continued improvement on unseen tasks, providing evidence of transfer beyond the
evolution settings.
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All authors Ziwen Li, Hanlue Zhang, Zhenyang Ren, Tianyu Huang, Runqi Lin, Haoyu Wang, Zhengqing Gao, Yandong Guo, Fakhri Karray, Tongliang Liu, Chris Russell, Mingming Gong
Artificial Intelligence
Flat-Consensus Diffusion for Robust Data Reshaping under Noisy Evaluator
Hongyu Cao, Kunpeng Liu, Fei Xie et al. (+1 more)
Data shape determines how features are structured, how patterns are separated, and how distributions cover the underlying domain. Poor data shape can make models learn noise rather than generalizable structure. This paper studies robust feature-centric data reshaping: generating feature transformations that remain useful, stable, and reproducible under noisy evaluation and imperfect data conditions. We view reshaping operation sequence search as reward-guided
diffusion generation, and robust reshaping as searching for regions in the latent reward landscape rather than isolated high-reward transformations. The key challenge is dual instability: noisy evaluators distort local reward guidance, while
stochastic generative trajectories can converge to inconsistent solutions. We propose FCDiff, a flat-consensus
diffusion framework that addresses both failures through a micro-macro decomposition. The micro layer replaces point-estimate reward guidance with Gaussian-smoothed, Monte Carlo averaged gradients, steering generation toward locally flat reward regions. The macro layer aggregates independently guided trajectories with a weighted Frechet-mean barycenter, selecting consensus-supported basins and filtering
stochastic outliers. Across an 8-
dataset headline cohort under heavy-tailed evaluator noise, FCDiff attains the best aggregate rank on lower-tail reliability and robustness against both search-based AutoFE and robustness-oriented generative baselines, with statistically significant accuracy gains over every generative baseline. Our results show that robust data reshaping requires searching for flat, consensus-supported regions rather than sharp single-trajectory optima.
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All authors Hongyu Cao, Kunpeng Liu, Fei Xie, Sandip Ray
Machine Learning · Cross-listed from stat.ML
Affine Geometry of Gaussian ReLU Networks via Conditional Kac-Rice Formulas
Recep Özkan and Christian Hirsch
We study how the affine geometry of finite ReLU networks is created at random initialization and reorganized by supervised training. We call a sign-changing zero of a hidden preactivation an activation switch and a point where the scalar network output is nondifferentiable a scalar kink. For one-dimensional input, conditioning on the preceding layers makes each preactivation Gaussian and affine on the
cells of a random finite partition. This yields an exact finite-width conditional Kac-Rice formula for the expected number of activation switches along an input interval. For fixed depth and proportionally growing widths, the resulting switch intensities converge to explicit deterministic limits. A visibility estimate shows that the expected number of switches that do not produce scalar kinks is negligible, yielding an explicit leading formula for the expected number of scalar kinks and hence affine regions. In higher input dimensions d >= 2, the analogous conditional surface formula yields the leading expected (d-1)-dimensional Hausdorff measure of the scalar kink set. On the Breast Cancer Wisconsin data, the initialization formula accurately predicts switch counts along held-out segments. After training, switch counts decrease along within-class segments and increase along between-class segments. Thus, training redistributes rather than merely contracts affine complexity.
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Artificial Intelligence
IGSD: Environment-Verified Hindsight Self-Distillation for Search Agents
Angqing Jiang, Gaoming Zhang, Chaoqun Zhang et al. (+5 more)
On-policy self-
distillation densifies
agent training without external teachers: a policy conditioned on privileged hindsight provides step-level guidance for its own unprivileged rollouts. For search
agents, however, hindsight can make the teacher prefer a query that does not improve
retrieval from the student's state. Existing methods either distill this preference directly or filter it with model-internal scores, but neither strategy verifies the query's executed
retrieval consequence. We propose Information-Gain-Gated Self-
Distillation (IGSD), which verifies on-policy
token proposals with environment feedback before distilling them. Treating each query
token as a micro-action, IGSD completes the teacher's
token proposal and the student's sampled
token into matched queries and executes both from the same failed state with the same retriever. Shared counterfactual controls account for query-conditioned shifts in answer likelihood, so their difference, the executed paired information gain, provides a relative utility contrast for the retrieved documents. IGSD uses this contrast as a positive-only soft weight for candidate-pair
distillation, while leaving the GRPO objective unchanged and confining verification to training. Across seven single-hop and multi-hop QA
benchmarks, IGSD reaches macro-average exact-match accuracies of 42.8% and 47.0% with 3B and 7B policies, respectively, without inference-time verification. These results support environment-verified hindsight as an effective approach to reliable action-level supervision for search
agents.
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All authors Angqing Jiang, Gaoming Zhang, Chaoqun Zhang, Jianchun Song, Liyuan Kong, Kena Qi, Wei Lin, Defu Lian
Artificial Intelligence
World Agent: Can Language Models Keep a World Running?
Weixing Chen, Weipeng Zhang, Nan An et al. (+2 more)
World models are moving from generating realistic frames to generating playable worlds, yet whether a delivered world can keep running is not tested anywhere. Existing evaluations stop at generation, at delivery, or at single-step transitions, and each stops at a different point along the way. Correct local state transitions or intermediate outcomes do not guarantee a correctly organized causal event flow. We propose the world
agent task, which moves the evaluation point of world generation from the moment of delivery to the continued operation that follows. In this task, a model is not asked to generate a world. It is held responsible for keeping the world running, which requires coordinating events and carrying forward their consequences to constrain subsequent
evolution. We instantiate the task in WorldAgent-
Benchmark with two complementary tracks. In the maintenance track, the model must ground the events of a continuous narrative into correct transitions of the explicit world state while respecting causal, temporal, and concurrency constraints. In the deduction track, the model must predict how the world will evolve under partial observations and act toward a goal. The maintenance track combines LLM-assisted semantic judgments with programmatic validation and scoring, while the deduction track is evaluated entirely programmatically. Individual judgments are auditable against world states and execution logs, and scores can be recomputed from the saved judgments and execution records. Across 8 models, scores decline steadily as pre-built structure is removed from the world, and causal-relation checking is the weakest component for every model. The
benchmark makes the continued operation of a world measurable and distinguishes local completion from failures in event organization. Code and
dataset will be released on https://github.com/HCPLab-SYSU/WorldAgent-
Benchmark.
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All authors Weixing Chen, Weipeng Zhang, Nan An, Yang Liu, Liang Lin
Cryptography and Security
Silent Failures in Agentic Security Evaluation: A Validated Harness for Tool-Call Mediation Under Indirect Prompt Injection
Animesh Shaw
LLM
agents that invoke privileged tools are vulnerable to indirect
prompt injection (IPI), in which
adversarial instructions embedded in retrieved data hijack the
agent's actions. A growing body of work evaluates defenses against IPI, but the validity of that evaluation is rarely examined. We audit an IPI
benchmark and its harness and identify four defect classes – silent payload non-delivery, attack success scored by tool identity rather than arguments, false-rejection rate conflated with model incapacity, and the absence of an audit trail – each of which yields a plausible, publishable, and incorrect number. We quantify the distortion by re-scoring identical execution traces under the defective and corrected definitions: on real
agent behaviour, the tool-identity scorer reports a 21.7% attack-success rate where the true argument-level rate is 1.2%. In the sharpest case, an open model previously reported at 62.8% registers 0% under the corrected harness – the prior figure largely an artifact of undelivered payloads and identity-level scoring. We release a harness whose construction makes each defect unrepresentable – machine-checkable payload placement, argument-level attacker predicates, per-scenario environments, and mandatory trace persistence – and use it to report three quantities the field does not: whether a compromised
agent discloses the attack, the full security/utility operating curve of an LLM-judge defense, and tool-calling capability disentangled from defensive over-blocking. A corrected harness further overturns a reported "capability barrier": a model deemed incapable of tool use is in fact fully capable, its earlier result an artifact of environment mismatch. We argue that evaluation validity is a prerequisite for, not a footnote to, defense claims in agentic security, and provide an instrument that enforces it.
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Computer Vision
MM-OPD: Towards One More Bottleneck Between Perception and Reasoning
Jintao Tong, Yujing Lou, Zhanming Shen et al. (+6 more)
Recent
multimodal large
language models (MLLMs) advance visual
reasoning by strengthening both perception and
reasoning, implicitly assuming a process that transitions seamlessly from perception to
reasoning. However, we observe a counterintuitive phenomenon that challenges this assumption: holding the model, question, and decoding fixed, we replace images with their caption or code representations (symbolic views), which seems to be redundant given the clear image structures, but the performance surprisingly improves by 10.2% to 23.6% across model scales and
datasets. We term this performance gap as the Symbolic Visual Gap and then take a closer look at it. Through experiments, we find that although the visual evidence can already appear in the
reasoning trace for the image-input model, the symbolic-view-input model shows much higher
attention to the correct evidence than the image-input model. This suggests that despite good capabilities from current works in perception and
reasoning themselves, another bottleneck exists between perception and
reasoning in selecting perceived visual information as appropriate evidence for subsequent
reasoning. To handle this bottleneck, since the symbolic view steers
attention toward correct evidence and is readily obtained at scale, it provides supervision for evidence selection without manually labeled evidence. Building on this, we introduce MM-OPD, a
multimodal on-policy self-
distillation framework for symbolic-to-visual correction that transfers guidance from symbolic-conditioned behavior to the image-conditioned policy through residual
token-level targets, steering the model toward correct visual evidence. Experiments across
benchmarks and model scales show that MM-OPD improves a broad range of
multimodal abilities, with gains in visual perception, chart and document understanding, mathematical
reasoning, and general VQA.
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All authors Jintao Tong, Yujing Lou, Zhanming Shen, Jiaqi Gu, Lubin Fan, Ruixuan Li, Yue Wu, Jieping Ye, Yixiong Zou
Machine Learning
Self-Evolving Time-Series Forecasting Agents with Episodic Memory and Online Policy Learning
Junyi Wang, Yilin Wang, Wen Wu et al. (+1 more)
LLM-based
agents are increasingly used for time-series
forecasting because they can organise contextual information, perform multi-step analysis, and guide the sequence of actions required to complete
forecasting tasks. Most existing
agents focus only on the current
forecasting instance. However, in real-world deployments,
forecasting commonly operates online, with new forecasts issued from the currently available history as the forecast origin advances and the ground-truth targets of earlier instances progressively become available. These targets provide feedback on the actions taken in earlier instances, yet existing
agents generally do not preserve or utilise this information to adapt their subsequent actions. To address this limitation, we introduce FASE, a Feedback-Aware Self-Evolving
forecasting agent that converts such feedback into task-specific experience for subsequent
forecasting instances. FASE combines episodic
memory, which retrieves relevant completed instances, with online policy learning, which summarises the feedback accumulated across instances into ranking guidance. The proposed framework is evaluated on 29
dataset configurations selected from the GIFT-Eval
benchmark. Across these 29 configurations, FASE attains the strongest aggregate point
forecasting performance among the evaluated methods and reduces the normalised MAE by 9.1% relative to the best individual foundation model baseline. The results further indicate that the cumulative advantage of FASE increases as delayed feedback accumulates. Together, these findings demonstrate that FASE can continually self-evolve through feedback from completed
forecasting instances without updating the parameters of the LLM.
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All authors Junyi Wang, Yilin Wang, Wen Wu, Chao Zhang
Artificial Intelligence
Dude, Where's My State? Execution Information Requirements for Stateful Agents
Nikita Mehrotra, Ashish Tiwari, Priyanshu Gupta et al. (+1 more)
Long-running
agents must preserve information that later steps depend on. We introduce the Execution Information Requirement (EIR), a lower bound on the information that must remain accessible for correct completion under specified task and access conditions. We develop LACUNA, a framework that generates tasks with known dependencies and varies information demand, retention, and recovery separately from the difficulty of individual operations. Across four models, restoring a missing result raises accuracy on affected recall steps to 100%, compared with 0% for equal-length irrelevant information. Sufficient storage alone does not ensure success: retention policies can discard required results, errors can propagate through later computations, and
agents can stop before recovery is complete. We also introduce VESTIGE, which uses
agent execution traces to construct semantic graphs and measure information demand for real tasks. Across 72,562 software-
agent trajectories, VESTIGE reveals a steeper distance-related decline in solution-relevant rereading for failed runs (RR 0.951 per distance doubling), while adjusted peak demand alone is not associated with failure. Together, these contributions support evaluating whether
agents preserve and recover the information their tasks require.
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All authors Nikita Mehrotra, Ashish Tiwari, Priyanshu Gupta, Sumit Gulwani
Artificial Intelligence
PINNMorph: Evolving Online Adaptation Policies for Physics-Informed Neural Networks
Xu Yang, Mingyang Yu, Jun Zhang et al. (+2 more)
Physics-informed neural networks (PINNs) provide a learning-based framework for solving partial differential equations (PDEs), yet their training behavior can change substantially throughout optimization. Residual distributions, gradient interactions, regional learning difficulty, and model-capacity requirements may evolve over time, while the network architecture and major training mechanisms are typically determined before training. We propose PINNMorph, an online PINN adaptation framework based on large
language model (LLM)-guided policy
evolution. PINNMorph maintains a population of state-conditioned adaptation policies that map execution diagnostics to controlled interventions over
topology modification, additive representation augmentation, objective balancing, gradient handling, adaptive sampling, and optimizer-phase control. At each intervention opportunity, candidate programs are instantiated from the current policy population, selected according to the observed training state, and applied directly to the PINN under training. The resulting model inherits its existing parameters and training state and continues optimization along the same trajectory. Execution outcomes are subsequently used to evaluate interventions and evolve the policy population. Unlike pre-training architecture search or fixed adaptation rules, PINNMorph jointly adapts the current PINN and the policies governing its interventions using feedback from actual training. Experiments on 13 PDE
benchmarks show that PINNMorph achieves lower solution errors than SA-PINN, ConFIG, RoPINN, HARMONIC, and PINNsAgent across all evaluated problems. Ablation studies further examine the effects of online adaptation, state-conditioned intervention selection, and execution-feedback-driven policy
evolution.
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All authors Xu Yang, Mingyang Yu, Jun Zhang, Keqian Li, Jing Xu
Computation and Language
Focusing Condition: Inference-Time Self-Contrastive Steering Elicits Better Conditional Text Embeddings in LLMs
Zifeng Cheng, Lingyun Qian, Zhiwei Jiang et al. (+5 more)
Extracting conditional text
embeddings from large
language models (LLMs) is a promising paradigm, as it requires neither additional data nor fine-tuning. Existing methods incorporate conditions into
prompts to guide LLMs to focus on specific aspects and elicit conditional text
embeddings. However, relying solely on
prompts often fails to produce high-quality conditional text
embeddings, as they remain entangled with general text
embeddings, ultimately degrading their quality. To this end, we propose an inference-time, plug-and-play Self-Contrastive Steering (SCS) method that constructs unconditional general text
embeddings and uses them to refine conditional text
embeddings, making them more focused on the target condition. Specifically, we modify the
attention mask and positional encodings to mask the condition, thereby obtaining unconditional text
embeddings and intervening in the multi-head self-
attention computation process. Notably, our method is highly efficient, requiring only a single additional multi-head self-
attention computation at inference time. Extensive experiments on clustering, Semantic Textual Similarity, and triplet
alignment datasets demonstrate that our method can seamlessly improve the performance of existing
prompt-based methods across different LLMs in a training-free and plug-and-play manner. Our code will be released at https://github.com/zifengcheng/SCS
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All authors Zifeng Cheng, Lingyun Qian, Zhiwei Jiang, Cong Wang, Yafeng Yin, Fei Shen, Ao Zhou, Qing Gu
Computer Vision
RCVLA: 4D Radar-Grounded Semantic Reasoning and Trajectory Arbitration for Autonomous Driving
Lianqing Zheng, Xiaokai Bai, Yixuan Luo et al. (+6 more)
4D radar provides
geometric and motion cues that complement visual semantics, but integrating it into
vision-
language-action (VLA) models requires both radar–
language alignment for semantic
reasoning and explicit use of radar measurements for trajectory refinement and selection. To support these capabilities, we construct Cap4DR with 86,016 radar-image-text samples for
alignment pretraining and OmniHD-QA with 520,161 question-answer pairs for instruction tuning across scene description, key-object
reasoning, occupancy understanding, and trajectory planning. Building on these
datasets, we propose RCVLA, a radar-camera VLA framework consisting of a radar-grounded semantic
reasoning stage (RCVLA-Sem) and a trajectory arbitration stage (RCVLA-Phys). RCVLA-Sem performs gated bidirectional interaction between camera and radar
tokens for driving question answering and reference trajectory generation, while auxiliary heads provide object and occupancy queries. RCVLA-Phys refines reference-guided trajectory candidates through truncated
diffusion conditioned on these queries and cluster-level radar measurements, then calibrates candidate scores using radar-derived time-to-collision risk. On OmniHD-QA, RCVLA-Sem improves CIDEr by 9.92 points and reduces key-object velocity error by $21.9\%$ relative to OmniDrive. RCVLA-Phys further reduces average L2 error from $0.348$ to $0.259\,\mathrm{m}$ and average open-loop collision rate from $0.576\%$ to $0.175\%$ relative to RCVLA-Sem. Ablation studies further show that
language-aligned radar
tokens improve semantic
reasoning, while cluster-level radar measurements and risk calibration improve trajectory arbitration. Code will be released.
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All authors Lianqing Zheng, Xiaokai Bai, Yixuan Luo, Runwei Guan, Minghao Liu, Zhiqiang Wei, Hui-liang Shen, Xichan Zhu, Zhixiong Ma
Machine Learning
The GUI Is Not the State: Diagnosing State Aliasing in GUI World Models
Dongsheng Liu, Chao Jin, Wenkui Yang et al. (+7 more)
GUI World Models (GUI-WMs) are increasingly used to predict future states for
agent planning and simulation, yet most existing formulations condition only on the current GUI observation and action. We identify state aliasing, where the vis- ible interface omits transition-relevant environment state, so identical observable conditions can correspond to different valid futures. To diagnose this failure mode, we introduce StateAliasBench, a diagnostic
benchmark that explicitly isolates such ambiguities via strict pairing. We further propose lightweight predictive- state recovery that infers structured state from history and augments otherwise frozen GUI-WMs through a deterministic state interface. Family-specific special- ists provide state recovery across heterogeneous state types, and multi-teacher dis- tillation consolidates them into a single unified
estimator. Experiments show that existing GUI-WMs exhibit systematic failures under observation-only condition- ing, while predictive-state augmentation substantially restores state-sensitive pre- diction across evaluated WMs, preserves generative fidelity, and improves down- stream performance of GUI
agents on AndroidWorld. These results suggest that reliable GUI world modeling should account not only for what is visible, but also for the hidden transition state that determines what happens next.
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All authors Dongsheng Liu, Chao Jin, Wenkui Yang, Hejin Wang, Junwei Yang, Zeren Zhang, Ziwei Chen, Huaibo Huang, Jie Cao, Ran He
Artificial Intelligence
When Better Gets Worse: Improvement Fidelity for Self-Improving Agents in Adaptive Worlds
Ke Wang, Zijie Zhao, Zhiyi Yuan et al. (+1 more)
Self-improving
agents increasingly rely on proxy verifiers to choose policy updates, yet deployment can change the world in which those updates are evaluated. An update that looks better to the verifier can therefore become worse after deployment even when the verifier ranks policies well overall. We formalize this gap as Improvement Fidelity, which asks whether proxy improvements preserve the sign and ordering of deployment improvements over the updates an improvement process actually proposes. We show that global policy accuracy need not guarantee update fidelity: operator shift and deployment response can create update-level errors, while candidate margins determine whether those errors change the replacement decision. We introduce PIVOT-KG, a paired, decision-aware validator that allocates scarce high-fidelity evaluation according to the expected reduction in selection regret per unit cost. Across 90 held-out roots in Leduc, Kuhn, and Melting Pot, proxy and deployment optimal sets are disjoint in 51 cases. In an eight-candidate HighwayEnv stress test, PIVOT-KG reduces mean improvement-selection regret from 0.0435 under the exact Uniform validation rule to 0.0055 at the primary budget. Together, these results show why reliable self-improvement should evaluate proposed improvements in the worlds they induce, while providing a practical rule for allocating scarce deployment evidence when it can affect the replacement decision.
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All authors Ke Wang, Zijie Zhao, Zhiyi Yuan, Changlun Li