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版本更新 · v3arXiv:2604.18820

Sparse Network Inference under Imperfect Detection and its Application to Ecological Networks

Aoran Zhang, Tianyao Wei, Maria J. Guerrero, César A. Uribe

原文摘要

Recovering latent structure from count data has received considerable attention in network inference, particularly when one seeks both cross-group interactions and within-group similarity patterns in bipartite networks, which are widely used in ecology research. Such networks are often sparse and prone to imperfect detection. Existing models mainly focus on interaction recovery, while the induced similarity graphs are much less studied. Moreover, existing models for under-counted data place sparsity, if at all, on the detection model rather than on these graphs. To address these issues, we propose a nonnegative low-rank factorization framework that jointly estimates detection probabilities and promotes sparse within- and cross-group graphs through $\ell_{1/2}$ regularization. For the resulting nonconvex, nonsmooth problem, we develop an ADMM-based algorithm with adaptive penalization, and establish asymptotic feasibility and KKT stationarity of cluster points under stated assumptions. We also give sufficient conditions for exact recovery of within- and cross-group graphs. On synthetic data with up to 20% missing entries, the method recovers the graphs and detection probabilities with competitive accuracy. Across three ecological datasets, it matches the baseline held-out predictions while estimating detection probabilities and within- and cross-group graphs.

stat.ML · cs.LG · eess.SP · math.OC · stat.AP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08845

Jev as an LLM Safety Guardrail: An Empirical Study of Input Filtering, Selective Output Review, and Over-Refusal

Olivia Cognetti, Bhaskar Krishnamachari

原文摘要

External safety filters can prevent unsafe requests from reaching a large language model (LLM), but may also reject legitimate queries. We present an end-to-end empirical evaluation of Jev, a probabilistic decision model, as a safety guardrail for a Qwen2.5-1.5B-based pipeline. Three configurations are compared on 970 benchmark prompts: an unfiltered LLM, Jev input filtering, and tiered Jev input filtering with selective output review. Across 720 prompts labeled harmful (520 AdvBench and 200 XSTest-unsafe), the fraction recorded as answered decreases from 527/720 (73.2%) without filtering to 17/720 (2.4%) with input filtering. On 250 XSTest-safe prompts, however, non-answer/refusal increases from 3/250 (1.2%) to 27/250 (10.8%). Tiered filtering yields 40/720 (5.6%) harmful-prompt answers and 10/250 (4.0%) benign-prompt refusals, with 79/970 (8.1%) of prompts requiring output review. Joint optimization of the tiered thresholds improves macro-F1 from 0.9351 to 0.9512, while reducing harmful-prompt answers and output-review frequency at the same benign-refusal rate. Matched-budget comparisons reveal modest additional gains over a retuned single gate on the evaluation data, although these gains are not consistently observed on a held-out split.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.17095

Computational Construction and Engineering Evaluation of a Verified Mono-Monostatic Body

Vincent Wesley Couey

原文摘要

Many engineering failures in orientation-dependent systems are geometric failure modes: changing the material delays the failure; changing the geometry can eliminate it. The mono-monostatic property (exactly one stable equilibrium under gravity) is mathematically proven to exist in convex homogeneous bodies, but, to our knowledge, no verified smooth geometry had been openly published when v1 appeared (April 2026). We introduce an Equilibrium Count Score (ECS) oracle that measures stable equilibria via drainage basin analysis on the center-of-mass height landscape. v2 correction: v1 reported that Sloan's (2023) analytical Gomboc parameterization shows 4-11 local minima "at all published parameter values." That claim is withdrawn. The beta values swept were this work's own grid points, not Sloan's (he states convexity bounds beta <= 0.15 and beta <= 0.17); they lie inside the strictly convex sub-regime beta < ~0.036, where a body at beta = 0.023 was later certified mono-monostatic by interval arithmetic; and the oracle's two stages are biased in opposite directions, so its count can be wrong either way. We construct one strictly convex mono-monostatic body by extending the Sloan phase function with a Fourier term and optimizing via differential evolution, with ECS=1 confirmed across merge thresholds from 0.5% to 10%; two more reach ECS=1 under the oracle but are not strictly convex. Conventional geometries cannot achieve ECS=1 through ballast alone: cylinders and cubes retain multiple stable equilibria even at 30% bottom-weighted mass. Applications: an IMU calibration housing (349x orientation precision improvement), aerial reforestation seed pods (20-67% germination loss from incorrect landing orientation), and marine buoy self-righting. Cross-layer scoring shows the Gomboc is 11.8x worse than the cylinder on contact distribution while optimal on equilibrium stability.

cs.CE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08840

What Matters for LLM Persona Simulation: Signal Form in Cold-Start E-Commerce Advertising

Jiwoo Choi, In Ik Lee, Woo Sung Seo, Minho Lee, Yun Young Choi

原文摘要

Estimating advertising performance in cold-start settings is challenging because historical logs are unavailable before launch. Large language models (LLMs) offer a potential alternative by simulating persona-conditioned user responses, but it remains unclear which design choices actually drive LLM-based persona simulation. Using real Google Ads e-commerce campaign data across five demographic segments, we identify three signal-form dimensions that affect simulation quality: (i) a demographic anchor without noisy rich attributes, (ii) a relative qualitative banner-blindness signal rather than quantitative anchors, and (iii) prompt phrasing. On this campaign, our method attains an MAE of 24.9 and a MAPE of 26.1%, a 51.3% lower MAE than the best tested non-LLM baseline and 77.3% lower than an LLM configuration using explicit CTR anchors. Systematic ablations show that minimal demographic descriptions achieve lower mean error than richer persona configurations, while relative qualitative cues substantially outperform the tested quantitative anchors. We additionally compare these configurations with direct-prior and rule-based baselines and with alternative LLM configurations.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.41675

Radial Kernel Attention for Global Feature Exchange in Polymer Machine Learning Force Fields

Wenbo Fu, Chenghui Mao, Haifeng Lv, Chengwen Hu, Zhou Lu

原文摘要

We introduce Radial Kernel Attention (RKA) for long-range modeling in polymer machine learning force fields. Existing approaches often rely on local neighbor graphs, which limit communication across polymer chains and their surrounding environments, whereas explicit all-pair attention incurs quadratic computational cost. RKA maps interatomic distances from the cluster centroid to learned radial features and combines their kernelized overlap with content-dependent queries and keys. Factorized aggregation and readout enable global feature exchange at linear complexity in the number of atoms for fixed feature dimensions. To ensure physically consistent predictions, we insert the invariant scalar update between EquiformerV3 local blocks and obtain conservative forces by differentiating the total energy, including its dependence on radial coordinate . Our models are trained on official OPoly26 training set for 12 epochs and evaluated directly after training on the complete official validation set. A matched 5M comparison shows that RKA reduces per-atom energy and force MAEs by 67.4% and 36.3%, respectively. The 20M RKA model attains a total-energy MAE of 15.81 meV (per-atom MAE of 0.1182 meV/atom) and a force MAE of 2.619 meV/Angstrom,improving all three metrics over UMA-S-1.2 and UMA-S-1.2.1 on the same validation structures. RKA provides a compact pathway for information exchange beyond local graph connectivity in polymer machine learning force fields.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08837

Solver-Admitted Metamorphic Testing of Learned Network Optimization Surrogates

Vineet Jagadeesan Nair, Stephen Peng, Arun Ravindran

原文摘要

Equivalent encodings of an optimization problem can produce different learned predictions. Interpreting these differences requires accounting for numerical error and multiple optima. We construct metamorphic tests for optimization solver surrogates with explicit eligibility conditions, output alignment, and numerical thresholds. Reference-solver agreement and independent constraint checks determine which sampled pairs enter the audit. We study alternating-current optimal power flow (AC-OPF), the traveling salesman problem (TSP), the capacitated vehicle routing problem (CVRP), and maximum independent set (MIS). Across five grids, GridSFM-Open violates each of 13 relations on at least one grid, and 12 by more than twice the threshold. Randomly initialized copies of the grid models fail nearly the same relations, so the failures do not require training. Source patches and input canonicalization remove selected discrepancies, but no intervention establishes preserved accuracy and feasibility within the declared margin on every grid. In routing, Policy Optimization with Multiple Optima (POMO) has a single-pass rotation/reflection violation rate that rises from 12.7% to 89.5% between 20 and 100 cities. Graph-Based Diffusion Solvers for Combinatorial Optimization (DIFUSCO) violates the MIS disjoint-union relation on 35.6% of draws. Post hoc normalization within connected components removes these violations on the tested graphs. These findings support reporting metamorphic consistency alongside optimality, feasibility, and accuracy, and validating repair quality separately from consistency.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08836

Hardness of Approximation for Polynomial Optimization over Sphere for Linear Programming Relaxation Hierarchy

Mrinalkanti Ghosh

原文摘要

Semidefinite programming (SDP), particularly the sum-of-squares (SoS) hierarchy, provides $n^{O(q)}$-time approximation and certification algorithms for optimizing degree-$d$ homogeneous polynomials on the unit sphere. For arbitrary inputs, degree-$q$ SoS gives a powered-objective approximation factor $O_d((n/q)^{d/2-1})$ [BGGLT17]. For random order-$d$ tensors, degree-$d$ SoS has an integrality-gap lower bound of $2^{-O(d)}(n/d)^{d/4-1/2}$ relative to the true spherical maximum; lower bounds also extend to growing SoS degree in tensor PCA [BGL17,HKP17]. We establish lower bounds for weaker linear-programming relaxation hierarchies. Our degree-$q$ moment LPs impose the sphere identities and any prescribed, instance-independent family $\mathcal{G}$ of valid polynomial inequalities of degree at most $q$. They include Sherali--Adams-type product hierarchies. For every even $d\ge2$, constant $C>0$, and $q$ satisfying $2d | q$ and $2d\le q\le n$, every such relaxation with at most $n^{Cq}$ tests has instances $P$ satisfying \[ \frac{\mathrm{PowLP}_{q,\mathcal{G}}(P)}{\|P\|_\infty}\ge \frac{c_{d,C}}{\sqrt{q\log n}}\left(\frac nq\right)^{d/2-1/2}.\] Here $\|P\|_\infty$ is the maximum absolute value of the polynomial over the sphere and $\mathrm{PowLP}_{q,\mathcal{G}}(P)$ is the relaxed objective. The exponent $d/2-1/2$ improves the leading dependence on $d$ relative to the $d/4$-type exponents in prior random-tensor SoS lower bounds. Our LP hierarchies impose weaker positivity conditions than semidefinite moment relaxations. It leaves open whether analogous degree dependence can be achieved in lower bounds for SDP hierarchies.

cs.CC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08834

Notwithstanding Anything to the Contrary, Ignore All Instructions: Measuring Prompt Injection Guards on Legal Contract Review Attacks

Mark Heimann, Katherine Taylor, Bruno Jacob

原文摘要

Specialized prompt injection guards are deployed in front of document analysis systems, but no false positive rate has been published for any of them on legal contracts. We measure eleven open-weights guards on two corpora of legal contracts and find that existing guards struggle: document-level false positive rates are as high as 92.39%; two guards fall below chance on paired AUC; and a five-regex baseline is not Pareto-dominated by any guard in the panel. Controlled probes locate model-specific as well as general failure modes. Prompted LLMs resolve the tested failures, including a second threat category (unauthorized scope claims) on which every guard scores zero. A cascaded LLM solution, where the positives of a mid-tier model are verified by a frontier model, is 6.5x cheaper than using exclusively a frontier model and approaches its performance.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08833

Beyond the Lower-Star Filtration: Alternative Filtration Methods for Persistent Homology

Innocent Abaa, Umar Islambekov

原文摘要

In the lower-star filtration (LSF), simplices are assigned filtration values according to the maximum-vertex rule, with each simplex entering the filtration when its highest-valued vertex appears. A limitation of this construction is that some higher-dimensional simplices may enter simultaneously with their boundary faces, causing certain topological features to be filled immediately upon formation and thus not appear in the resulting persistence diagram. To address this limitation, we propose three alternative filtration methods: the \(\varepsilon\)-lower-star filtration (ELSF), the \(L_p\) filtration (LpF), and the softmax filtration (SMF). {ELSF introduces a dimension-dependent delay to the maximum-vertex rule,} allowing higher-dimensional simplices to enter the filtration after their boundary faces, whereas {LpF and SMF use aggregation functions that depend on all vertex values rather} than solely on their maximum. By modifying how filtration values are assigned, these methods allow simplices to enter the filtration more progressively, so that topological features such as loops and voids may persist over longer filtration intervals rather than being filled immediately. Consequently, the resulting persistence diagrams may include persistence features that are not observed under LSF, potentially providing useful information for distinguishing data populations. Experimental results on simulated and real-world datasets demonstrate that the proposed filtration methods are computationally efficient and can provide greater sensitivity to structural differences in the data than LSF.

stat.ML · cs.LG · math.AT · stat.AP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.03194

TATG: Tracking-Aware Testing Objective for LLM-based Test Generation

Guancheng Wang, Qinghua Xu, Lionel Briand

原文摘要

Complex Java methods challenge automated unit test generation, as achieving high coverage and fault detection requires satisfying intricate testing requirements involving program states, execution paths, and assertions. Existing LLM-based approaches leverage program context, static analysis, and dynamic feedback but lack explicit tracking of individual testing requirements across iterations, potentially leading to redundant generation and unresolved testing objectives. We present TATG, a tracking-aware LLM-based unit test generation framework that unifies structural coverage and mutation-based fault detection. TATG introduces a structured objective representation capturing testing requirements derived from static analysis and dynamic feedback, enabling fine-grained tracking of their satisfaction status. It employs a two-stage workflow that first improves structural coverage and subsequently strengthens assertions through mutation-guided hardening. We evaluate TATG on 141 complex Java methods against state-of-the-art baselines, including KTester and PANTA. Results show average improvements of 22.15, 20.14, and 37.66 percentage points in line coverage, branch coverage, and mutation score, respectively. TATG also outperforms general-purpose coding assistants, including Codex and Claude Code, under the same open-weight model configurations, achieving higher average coverage and mutation scores while generating fewer tests and consuming fewer tokens. These findings highlight the value of explicitly tracking testing objectives for effective LLM-based test generation.

cs.SE

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2606.09289

Intention Driven Identification of In-Possession Match Phases in Association Football through Temporal Graph Learning

Yuesen Li, Daniel Link

原文摘要

Recognising intentions from continuous movement trajectories is an important problem in spatiotemporal data mining. This becomes difficult when behaviours change over time and their boundaries cannot be directly observed. Football provides a useful setting for studying this problem because tactical intentions emerge from the coordinated movements of multiple players during possession. Existing methods for match phase recognition often classify individual frames, use predefined spatial rules, or identify only selected tactical patterns. They therefore cannot fully describe continuous possession sequences in which tactical intentions change over time. We address this gap by treating the identification of the in-possession match phase as a hierarchical sequence segmentation task. The framework includes three tactical intentions (Invade Opponent Space, Keep Possession, and Scoring) and six phases (Build Up, Progression, Counter Attack, Maintenance, Sustained Threat, and Finishing). We propose a Temporal Graph Attention Network (T-GAN) that combines player interaction graphs, contextual features, and Transformer based temporal modelling. The framework was evaluated on seven Bundesliga matches using leave-one-match-out validation and compared with rule-based, Random Forest, GNN, and Transformer baselines at both frame and sequence levels. The results show that temporal modelling is important for coherent phase segmentation, while graph modelling provides additional value for transition phases such as Counter Attack. The framework provides an interpretable method for discovering intention based behavioural segments from continuous player tracking data and supports automated tactical analysis in football.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08831

Global Solar Siting from Geospatial Embeddings

Abdullah Rehman, Quentin Paletta, Rui Song

原文摘要

Global solar photovoltaic (PV) capacity is rapidly growing, helping decrease our emissions and dependence on fossil fuels. However, optimally siting a solar plant remains challenging, requiring anticipation of geographical, urban, and infrastructure needs. Current methods rely on multi-criteria decision analysis (MCDA) using hand-picked geophysical layers and solar-resource products, however these strategies are not geographically transferable and do not capture the practical requirements of solar sites. We use geospatial embeddings, dense vectors representing geophysical properties of a location, to analyse 38,591 existing solar sites and the land they were built on to learn the latent solar suitability signal. We assess a set of linear and non-linear models and select a soft-voting ensemble of a random forest and a multilayer perceptron (RF+MLP ensemble), chosen for accuracy and large scale deployability. Validated against future installations in thirteen countries, the ensemble separates existing solar sites from random land with a median ROC-AUC of 0.90. Compared with published MCDA strategies, the ensemble increases ROC-AUC by 0.06-0.30 in every country. The ranking also concentrates real deployment: the most-suitable 20% of land holds 84% of estimated generation (annual energy yield) and 67% of installed capacity (potential power output). After applying standard siting constraints, 5.6% of global land (9.8% if cropland is permitted) remains highly suited to solar. The global 10 m resolution solar suitability dataset and code can be found here: https://github.com/Abdullahr29/Global-Solar-Siting.

cs.LG · cs.CV · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10056

Safety Direction Adapter: A Training-Free Post-Hoc Safety Patch for Fine-Tuned Open-Weight LLMs

Chansu Han, Takeshi Takahashi

原文摘要

Open-weight LLMs are routinely fine-tuned from public repositories, yet task-specific adaptation can silently erode safety alignment inherited from instruct-tuned bases. This is a supply-chain risk when organizations ship third-party checkpoints without full re-alignment. Prior work rarely audits diverse public fine-tunes under a shared multi-axis safety-utility protocol or offers training-free mitigation at deployment time. We propose Safety Direction Adapter (SDA), a training-free post-hoc safety patch that extracts a safety direction once from an aligned base and transfers it one-to-many within that base family as a small PEFT-compatible weight update, evaluated under the same multi-axis protocol. Across six direction extractors, we evaluate 14 checkpoints from two 1-2B lineages and probe 8B and 27B fleets (n=5 each) on 15 benchmarks (12538 prompts). On Llama, SDA improves the four-axis mean by about 2.3 points against a 4.2-point instruct-relative gap (about 55 percent recovery; partial Avg), with Safety and Cyber recovering toward or beyond instruct; Safety gains about 8.5 points against about 4.9 points mean degradation. On Qwen, degradation is larger and excess-driven; SDA still lifts Avg (about 36 percent of the gap) while trading Safety and Cyber against Excess and General. We report dual-use risks of publishing direction adapters and do not claim robustness to adaptive or white-box attacks.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2603.09722

A Novel Two-Parameter Penalty: Relaxation Degree Analysis and Sparse Signal Recovery

Ziwei Li, Wengu Chen, Huanmin Ge, Dachun Yang

原文摘要

In this article, we introduce a nonconvex two-parameter penalty function $P_{a,p}$, parameterized by $a\in(0,\infty)$ and $p\in(0,1]$, and the relaxation degree RD$_P$ for a separable nonconvex penalty function $P$. Based on $P_{a,p}$, we further propose the $P_{a,p}$ minimization framework for sparse signal recovery. This framework generalizes the TL1 minimization model established by S. Zhang and J. Xin (corresponding to the special case $p=1$) and provides a unified family of nonconvex penalty functions for sparse signal recovery. Using the sparse convex-combination technique, we establish both exact and stable sparse signal recovery under the restricted isometry property (RIP). To efficiently solve the resulting nonconvex optimization problem, we apply a modified iteratively re-weighted least squares method and the difference of convex functions algorithm (DCA) to develop the IRLSTLp algorithm for unconstrained $P_{a,p}$ minimization and prove some convergence results. Finally, numerical experiments compare recovery and runtime against single- and double-loop baselines, examine sensitivity to $(a,p)$, and assess the empirical correlation between RD$_{P_{a,p}}$ and recovery within the proposed family.

math.FA · cs.IT · math.CA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.31940

Simple Extensions of Single-Objective Acquisition Functions and Hedge Strategies for Multi-Objective Bayesian Optimization

Haris Moazam Sheikh

原文摘要

Multi-objective Bayesian optimization (MOBO) is commonly approached through specialized acquisition functions or scalarization schemes designed to explicitly account for trade-offs among non-preferential objectives. In this work, we show that such complexity might be unnecessary. We propose a framework that extends standard single-objective acquisition functions directly to the multi-objective setting through a hypervolume-based methodology. We further extend hedge strategies for acquisition functions, which are typically used only in single-objective optimization, to the multi-objective regime. Our approach requires minimal modification to existing Bayesian optimization pipelines and avoids the need for bespoke multi-objective formulations. We demonstrate how a broad class of commonly used single-objective acquisition functions and hedge strategies can be adapted in a principled manner to handle multiple objectives, while preserving their intuitive interpretation and computational efficiency. Empirically, we evaluate the proposed methods across a range of synthetic and real-world multi-objective benchmarks. Despite their simplicity, our extensions consistently match or outperform more complex state-of-the-art MOBO methods in terms of optimization performance and sample efficiency. These results suggest that effective multi-objective Bayesian optimization can be achieved by reusing and carefully extending well-established single-objective acquisition strategies, offering a simpler and more flexible alternative to existing approaches.

cs.LG · math.OC · stat.ML

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.17228

Causal Representation Learning from Multimodal Clinical Records under Non-Random Modality Missingness

Zihan Liang, Ziwen Pan, Ruoxuan Xiong

原文摘要

Clinical notes contain rich patient information, such as diagnoses or medications, making them valuable for patient representation learning. Recent advances in large language models have further improved the ability to extract meaningful representations from clinical texts. However, clinical notes are often missing. For example, in our MIMIC-IV cohort, 35.15% of patients have no eligible discharge summaries. In such cases, representations can be learned from other modalities such as structured data, chest X-rays, or radiology reports. Yet the availability of these modalities is influenced by clinical decision-making and varies across patients, potentially producing modality missing-not-at-random (MMNAR) patterns. We propose a causal representation learning framework that leverages observed data and informative missingness in multimodal clinical records. It consists of: (1) an MMNAR-aware modality fusion component that integrates structured data, imaging, and text while conditioning on missingness patterns to capture patient health and clinician-driven assignment; (2) a modality reconstruction component with latent alignment to preserve modality information in the learned representation; and (3) a multitask outcome prediction model with a rectifier that corrects for residual bias from specific modality observation patterns. Comprehensive evaluations across MIMIC-IV and eICU show consistent gains over the strongest baselines, achieving relative AUC improvements of up to 4.3% for hospital readmission and 2.7% for ICU admission.

cs.LG · cs.CL · stat.ME

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.16843

Counterfactual Distillation: Internalizing Reflective Experience into LLM Agent

Rui Ge, Yichao Fu, Yu-Yang Qian, Junda Su, Yiming Zhao, Hao Zhang

原文摘要

Large language models increasingly act as autonomous agents in long-horizon interactions, learning from final outcomes and environmental feedback. However, optimizing from such interaction experience remains challenging, as feedback is heterogeneous and rarely carries explicit reward information. Existing post-training approaches rely on outcome-driven methods, such as reinforcement learning with verifiable rewards (RLVR), which primarily exploit final success signals while leaving interaction experience and feedback underutilized. In sparse-reward, long-horizon tasks, this often results in distribution sharpening: the policy reinforces a narrow set of already-successful behaviors, without substantially improving the feedback-grounded agency needed for broader problem-solving capacity (e.g., Pass@k). We propose Counterfactual Distillation, a framework that converts exploration-derived experience into trainable policy supervision. Specifically, we organize exploration as a tree-structured search process, where the agent reflects on past decision points and makes new experience-guided decisions to form alternative branches. These corrections are then distilled as counterfactual targets: we train the model to predict the revised action from the original history without explicitly providing the experience, thereby introducing out-of-distribution behavior patterns and expanding exploration capacity. Across diverse interactive coding and agentic tasks, our method outperforms outcome-driven baselines such as GRPO and experience-based methods such as Early Experience, with gains of up to 14 percentage points over the base model on Pass@128. When interleaved with reinforcement learning updates, it further raises the performance ceiling, improving Pass@1 on Sokoban by 8 percentage points over extended GRPO. Our code is available at https://github.com/motigrez/Counterfactual-Distillation.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.14477

Test-Time Learning with an Evolving Library

Weijia Xu, Alessandro Sordoni, Chandan Singh, Zelalem Gero, Michel Galley, Xingdi Yuan, Jianfeng Gao

原文摘要

We introduce EvoLib, a test-time learning framework that enables large language models to accumulate, reuse, and evolve knowledge across problem instances without parameter updates or external supervision. Instead of adapting model parameters, our approach maintains a shared library of knowledge abstractions, including modular skills and reflective insights, automatically extracted from the model's own inference trajectories. To support continual improvement, we introduce a principled weighting and consolidation mechanism that jointly optimizes for immediate utility and long-term value. This allows simple, instance-specific abstractions to evolve into more general and reusable ones over time. Across challenging benchmarks in mathematical reasoning, code generation, and multi-turn agentic environments, EvoLib improves substantially over the top test-time scaling and learning methods without ground-truth feedback.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08814

It Takes Two to Tangram: Multi-Agent Object Centric Reinforcement Learning for Assembly

Paul Knaußt, Kay Pompetzki, Nico Bohlinger, Niklas Funk, Cristiana de Farias, Rolf Findeisen, Jan Peters

原文摘要

Geometric assembly requires fine spatiotemporal coordination to place parts without causing collisions or deadlocks under tight clearances. To address this, we present an object-centric reinforcement learning framework that treats each part as an agent and learns a shared policy for continuous assembly control. A graph neural network represents the pieces, their target poses, and their spatial relationships, and predicts translational and angular velocities for each component. Per-agent proximal policy optimization with a centralized graph critic directly learns motion and temporal coordination within a single feedback policy. We study this formulation through planar Tangram assembly, where pieces must slide and rotate into a given configuration while remaining on the assembly surface. Procedural generation of target configurations and randomized initial arrangements support training and evaluation across diverse assembly problems. Our experiments isolate the roles of relational structure and multi-agent optimization, and compare learned control with sampling-based planning. We demonstrate concurrent assembly in simulation and transfer the policy to sequential execution on a real robot, as our object-centric formulation decouples learned assembly coordination from low-level physical control. Project page: https://tangrambench.org/

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.08360

Embeddings for Preferences, Not Semantics

Carter Blair, Ariel D. Procaccia, Milind Tambe

原文摘要

Modern AI is opening the door to collective decision-making in which participants express their views as free-form text rather than voting on a fixed set of candidates. A natural idea is to embed these opinions in a vector space so that the substantial literature on facility location problems and fair clustering can be brought to bear. But standard text embeddings measure semantic similarity, whereas distances in facility location problems and fair clustering require what we call preferential similarity: a participant's agreement with a piece of text should be inversely related to their distance from it. Off-the-shelf embeddings inherit a coarse preference signal through a correlation between semantic and preferential similarity, but fail to capture preferences when the correlation breaks. We formalize this as an invariance problem: text embedding models encode both a preference-relevant signal (stance and values) and semantic nuisance (style and wording), and the two are observationally correlated, so a geometry that relies on nuisance can appear preference-correct even when it is not. We show that synthetic training data designed to break this correlation provably shifts the optimal scorer away from nuisance-dominated cosine and significantly improves preference prediction across 11 online deliberation datasets.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08812

Dynamic signatures of collective knowledge through navigation in a network with semantic structure

Abby Bertics, Elizabeth R. Chrastil, Jean M. Carlson

原文摘要

What distinguishes how humans navigate an information network like Wikipedia from what the underlying graph structure and language statistics alone predict? To study human collective navigation, we analyze 70,770 human trajectories through Wikispeedia, an online game where players navigate between articles to reach a target. To compare groups of trajectories rather than individual ones, we merge all trajectories leaving or entering a given article into a Flow Tree. Flow Trees compress collections of trajectories into hierarchies of group splits. We build forward and reverse Flow Trees for 815 of the most commonly traversed articles, representing divergence and convergence, respectively. We compare humans against four random-walker null models, either unweighted or weighted by graph structure, word frequency, or semantic similarity to target. Human Flow Trees are deeper with longer trunks and later splits, indicating players stay together longer than random walkers, whose Flow Trees tend to fan out immediately. An article's degree predicts 51.3-70.9% of the variation in Flow Tree branching factor for random walkers, but only 24.1-36.2% for humans. Taking what graph structure fails to predict as a two-dimensional coordinate system, we define four article types (beacons, funnels, forks, and corridors), distinguished by how humans enter and exit them. These human-defined types are not explained by out-degree or title-word frequency are, and are not recovered by any random walker. A fifth null model, built from first-order human click preferences, partially reproduces human Flow Tree shape but differs significantly in how much of that shape degree explains. We consider collective knowledge to be the structure shared by players who never communicate, beyond what graph structure imposes. It comprises first-order click preferences and a significant component that extends across clicks.

cs.SI · physics.soc-ph

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版本更新 · v5arXiv:2410.22729

Identifying Drift, Diffusion, and Causal Structure from Temporal Snapshots

Vincent Guan, Joseph Janssen, Hossein Rahmani, Andrew Warren, Stephen Zhang, Elina Robeva, Geoffrey Schiebinger

原文摘要

Stochastic differential equations (SDEs) are a fundamental tool for modelling dynamic processes, including gene regulatory networks (GRNs), contaminant transport, financial markets, and image generation. However, learning the underlying SDE from data is a challenging task, especially if individual trajectories are not observable. Motivated by burgeoning research in single-cell datasets, we present the first comprehensive approach for jointly identifying the drift and diffusion of an SDE from its temporal marginals. Assuming linear drift and additive diffusion, we show that non-identifiability can only arise if the initial distribution possesses generalized rotational symmetries. We further prove that even if this condition holds, the drift and diffusion can almost always be recovered from the marginals. Additionally, we show that the causal graph of any SDE with additive diffusion can be recovered from the identified SDE parameters. To complement this theory, we adapt entropy-regularized optimal transport to handle anisotropic diffusion and introduce APPEX (Alternating Projection Parameter Estimation from $X_0$), an iterative algorithm designed to estimate the drift, diffusion, and causal graph of an additive noise SDE, solely from temporal marginals, and demonstrate its effectiveness on simulated data from linear additive noise SDEs.

stat.ML · cs.LG · math.ST

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新论文 · v1arXiv:2611.08811

I Think, Therefore I Attack: Evaluating Thought Injection Attacks on Reasoning Agents

Parth Illendula, Garik Livingston, Zain Shamsi, Ravi Srinivasan, Alex Liu

原文摘要

Large Reasoning Models (LRMs) are one of the key components of current agentic systems. LRMs augment their output with an explicit chain-of-thought reasoning stage, introducing a new attack surface for thought injection, in which untrusted text resembling the model's own deliberation is fed back to the model. We present a two-part study of this attack, performed against the prevailing large, production class open-weight models (e.g., GLM-5.3, MiniMax-M2.5, Qwen3.8-Flash-Next). We first evaluate the effectiveness of static thought-injection prompts under different styles as well as tagged and untagged conditions and find that the effects of thinking tags and payload styles are all properties of the specific target model. In the second study, we exploit this model-specificity in a containerized agentic cyber range, using indirect injection prompts styled in the form of the target model's own reasoning traces to achieve attack success rates (ASR) up to 50% on operational objectives such as reverse-shell exfiltration and repository backdooring. We also find that the same malicious objective in standard prompt injection user voice never succeeds, and thinking tags suppress the agent's tendency to disclose the injection. Our results show that imitating a reasoning model's own thought style is effective for this style of attack, and that tag stripping is an unreliable defense.

cs.AI · cs.CR

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版本更新 · v3arXiv:2609.19150

Sampling Reveals Style: Unsupervised, Training-Free Discovery of Prompt-Conditional Stylistic Axes in LLM Activations

Ajit Mallavarapu, Ziwei Gu

原文摘要

Large language models (LLMs) encode rich stylistic structure in their hidden activations, but discovering which stylistic dimensions are salient for a given prompt typically requires supervised contrastive data. We present a training-free, prompt-conditional alternative: we repeatedly sample completions of a single prompt at elevated temperature, apply Principal Component Analysis (PCA) to the pooled hidden activations, and label the resulting axes automatically from the pole generations. We validate the discovered axes against 245 human-elicited stylistic annotations in a two-phase study. On our strongest model (Qwen-3.5-4B-Instruct), the top two axes match spontaneously requested human dimensions with 72.8% precision and 43.6% macro-recall, and 75.6% of validity ratings judge the axes' polar generations accurate to their labels, with 90.9% adjacent inter-annotator agreement. Discoverability is strongly model-dependent: both Qwen models and Llama-3.2-3B expose human-salient axes, while DeepSeek-7B-Chat drops to 35.3% precision, its leading components dominated by structural rather than stylistic variance. Simple PCA over a model's own decoding variance is thus an effective, low-cost probe of stylistic structure in LLM representations, one that also exposes sharp cross-model differences in how that structure is organized.

cs.CL · cs.LG

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新论文 · v1arXiv:2611.08809

Sensing-Based Communication: A Non-Orthogonal Passive Reflection Framework for ISAC

Xin Xiong, Mathew Salas, Sihua Shao, Chen Chen, Mostafa Hassanalian, Rafael Fierro

原文摘要

While integrating uplink without disrupting the primary downlink remains a critical challenge for existing reflective communication systems, this paper proposes a non-orthogonal passive integrated sensing and communication (ISAC) mechanism termed Sensing-based Communication. Originating from and validated within an optical retro-reflective system, this mechanism possesses an underlying wave-agnostic architecture with the potential to be generalized across other physical modalities. It leverages incident downlink signals for parasitic modulation via passive tags. By relying solely on fundamental on-off keying (OOK) reflection, the tag unifies the reflective sensing channel and the uplink communication channel, and simultaneously achieves both sensing and duplex communication. Consequently, our approach introduces uplink capability without sacrificing the downlink data rate or consuming additional spectrum resources. At the receiver, we design a joint decoding architecture combining cross-correlation computation and successive interference cancelation (SIC) to fully exploit the degrees of freedom in broadband signals for multi-user interference suppression. We derive closed-form expressions for the interference and bit error rate (BER) assuming zero-mean Gaussian signals. Using an experimental testbed, we validate the pivotal theoretical finding, the scaling relationship of SINR with signal bandwidth and cross-correlation time--where linear regression yields a strong goodness-of-fit with $R^2 = 0.9688$. Furthermore, evaluations under noise-like orthogonal frequency division multiplexing (OFDM) waveforms exhibit high consistency between theoretical derivations and simulation data ($R^2 = 0.9372$, mean absolute percentage error, $\text{MAPE} = 14\%$), confirming the model's accuracy and waveform generalizability.

cs.IT · eess.SP · eess.SY

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版本更新 · v2arXiv:2609.20797

The Strong Secretary Conjecture is True for Linear Matroids

Kristóf Bérczi, Shaddin Dughmi, Vasilis Livanos, José A. Soto, Victor Verdugo

原文摘要

We prove a $1/e$ guarantee for Matroid Secretary on linear matroids: each element of a fixed optimal basis is selected with probability at least $1/e$. This settles the strong secretary conjecture for this class of matroids. Our result holds both when the matroid is known in advance and when a linear representation is given online. In the known-matroid model, the result extends to fully modular extendable matroids. We also obtain $1/e$ guarantees for a more general model on linear matroids, both when the matroid is known and when its representation is revealed online. In XOS Matroid Secretary, agents with XOS valuations over the elements arrive in uniformly random order, and the algorithm must assign each arriving agent a bundle of elements. The bundles must be pairwise disjoint, and their union must be an independent set of the matroid. We obtain an expected total value of at least $1/e$ times the offline optimum. This model contains Group Matroid Secretary, in which the elements are adversarially partitioned into groups that arrive in uniformly random order, and the algorithm selects a subset of each arriving group. For this special case, we select each element of a fixed optimal basis with probability at least $1/e$

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08804

Generative Search and Recommendation at Shopify

Juhana Kangaspunta, Khalil Hajji, Darshan Fofadiya, Kyumin Park, Michael Garner, Anthony Maltsev, Ryan Synk, Jose Sepulveda 等 11 位作者

原文摘要

Search and recommendation at Shopify require retrieving relevant products from a changing catalog of billions of products under strict latency constraints. We present SID-LLM, which casts both tasks as generating semantic IDs: short sequences of content-derived tokens representing products. We jointly pre-train SID-LLM on catalog and behavioral data, giving search and recommendation a shared representation and training objective. We show that reinforcement learning further improves judged search relevance. Furthermore, distillation from task-specific experts improves recommendation quality over joint supervised fine-tuning without a statistically detected loss in search quality. Finally, for efficient serving, we pair multi-token prediction with a lightweight ranker, improving retrieval quality over our autoregressive baseline while reducing latency. We evaluate SID-LLM both as a standalone retriever and as a component of Shopify's production search and recommendation systems. As a standalone search system, SID-LLM achieves quality comparable to Shopify's existing multi-model search stack while meeting production latency requirements. Integrating SID-LLM into the search and recommendation stacks provides significant gains in both offline evaluations and online A/B tests. Together, these results demonstrate the practical value of semantic-ID generation as a unified approach to product discovery at scale.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08802

Scale as a Modality: Multi-Scale Pixel-Space Diffusion Transformers

Ruibo Chen, Kaishen Wang, Tianyi Xiong, Chenxi Liu, Yanshuo Chen, Heng Huang, Jian Du

原文摘要

Latent diffusion models rely on pretrained autoencoders that introduce lossy compression and constrain spatial granularity. Pixel-space diffusion avoids this bottleneck and enables flexible tokenization at multiple spatial scales, yet existing approaches largely adopt two-level coarse-to-fine architectures with limited cross-scale interaction. We introduce MS-pDiT, a multi-scale pixel-space diffusion transformer that treats spatial scale as a modality. MS-pDiT tokenizes the same noisy image at multiple patch sizes, processes each scale in a dedicated transformer stream, and enables bidirectional information exchange through compacted MM-DiT-style joint attention, with attention cost independent of the finest patch size. This design establishes the number of scales as a new architectural scaling dimension. On ImageNet $256\times256$, increasing the number of scales from two to five consistently improves FID from 4.39 to 2.31, 2.26, and 2.19, while adding an intermediate scale substantially outperforms increasing depth under comparable parameter budgets. Further analysis highlights the importance of bidirectional cross-scale interaction and late introduction of fine-grained streams. MS-pDiT achieves FID scores of 1.43 and 1.44 on ImageNet $256\times256$ and $512\times512$, respectively, and naturally extends to text-to-image generation, achieving 0.86 on GenEval and 83.9 on DPG-Bench.

cs.CV

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新论文 · v1arXiv:2611.10055

HeteroTask: Multi-Variant Task Scheduling and Execution on Distributed Heterogeneous Clusters

Hongzheng Tian, Alok Mishra, Haocheng Xu, Dejan Milojicic, Sitao Huang

原文摘要

Heterogeneous systems that combine CPUs and GPUs across multiple servers offer substantial compute capability but complicate scheduling. For DAG-based applications with multiple task implementations, effective execution requires coordinating variant selection, device placement, and data movement. Offline planning can exploit global dependency information, yet plans derived from isolated kernel profiles may diverge from execution because of runtime overheads. We introduce HeteroTask, a framework integrating kernel-wise profiling, PEFT-based multi-variant planning, host-buffer lifetime modeling, and schedule-driven MPI execution. A Bayesian-optimization loop calibrates empirical overhead corrections for repeated, fixed-DAG workflows on exclusively allocated nodes. We evaluate five benchmark and workflow-derived applications on up to six 8-GPU nodes. On two 8-GPU nodes, HeteroTask reduces application-level completion time by 19.7-72.1% relative to the compared StarPU implementations. For Canny Edge Detection, calibration reduces task-window makespan by 25.0% relative to planning without overhead corrections. Combined with CPU/GPU variant selection, calibration reduces it by 58.9% relative to an uncalibrated GPU-only execution.

cs.DC

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新论文 · v1arXiv:2611.08800

On the Few-Shot Adaptation of Vision-Language Models: A Few Gradient Signs Are All You Need

Nairouz Mrabah, Ismail Ben Ayed, Mahdi S. Hosseini, Eric Granger

原文摘要

The limited supervision available for the few-shot adaptation of vision-language models makes gradient estimation particularly noisy. We find that the gradient magnitudes are severely miscalibrated, while the signs of a small top-magnitude subset remain informative. This motivates SignSparseSGD and DynSignSparseSGD, two state-free optimizers that use gradient magnitudes only to select where to update and fixed-magnitude signs to decide how to update. The static variant fixes the support after the first minibatch. The dynamic variant refreshes it at every step. Across 11 datasets, three CLIP backbones, and five shot levels, the proposed methods are competitive with or outperform parameter-efficient and optimization-based baselines on average, while exhibiting markedly lower sensitivity to long training schedules. They introduce no task-specific modules, maintain no first- or second-moment optimizer states, and use one shared configuration across datasets, backbones, and shot levels. Our code is included in the supplementary materials and will be released upon publication.

cs.LG · cs.CV

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