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版本更新 · v2arXiv:2610.32813

HarnessMM: Recursive Self-Improvement for Harness Token Efficiency

Yuhua Jiang, Chang Xu, Feifei Gao, Li Zhao

原文摘要

HarnessMinusMinus (HarnessMM) is a research project on improving the efficiency of agent harnesses, the software coordinating model reasoning, tool execution, and feedback. This technical report consolidates its shared framework, implementations, and empirical studies across coding, tool-based, and visual-agent settings. Agent harnesses often invoke reasoning after each tool result, even when earlier calls have already determined subsequent actions. HarnessMM addresses this overhead through guarded decision reuse and recursive self-improvement (RSI). RSI refines author prompts, context settings, and adapter rules using training feedback, with validation selecting deployment configurations. At inference time, a controller reuses resolved decisions while keeping actor weights fixed. Executable Decision Graphs encode actions, arguments, dependencies, and guards. Guarded Decision Reuse activates eligible decisions from observed feedback, while Certified Callback Substitution executes them through native tool interfaces, replacing redundant reasoning callbacks. The original actor resumes when new reasoning is required or reuse conditions are unmet. On HumanEval, HarnessMM preserves Codex success at 90.82\% while reducing tokens per task from 66.72k to 47.02k (29.54\% saving). For DeepSeek Harness, success improves from 71.43\% to 85.71\% while tokens decrease from 96.28k to 11.62k (87.93\% saving). An MBPP ablation shows 26.91\% lower cost than single-use graphs and 19.52\% lower cost than retained callbacks. The report also summarizes the HarnessMM-V visual-agent study and documents optimization overhead and execution time where measured. Together, these studies investigate guarded decision reuse across diverse agent harness settings. Code is available at https://github.com/YuhuaJiang2002/HarnessMM.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09649

Parallel Algorithms for Higher-Order Temporal H-Index Computation

Lutz Oettershagen

原文摘要

Ranking vertices by importance is a fundamental task in network mining. The H-index provides such a ranking by asking how many neighbors are themselves well connected; its higher-order temporal variant recursively applies this idea over time-respecting neighborhoods in temporal networks. Scaling its computation is difficult because the existing streaming algorithm intertwines temporal processing with H-index order, incurring maximum-degree-dependent costs and exposing little parallelism. We show that the temporal dependencies can instead be resolved once: each edge's time-dependent lookup is mapped to a fixed successor position, turning every H-index order into a parallel gather followed by a segmented suffix-H computation. Based on this reformulation, we derive a linear-work suffix-H algorithm, a parallel multicore CPU implementation, degree-adaptive GPU kernels, and a multi-GPU scheme that exchanges only distinct remotely queried states. Experiments on synthetic and real-world temporal graphs show that the reformulation alone is substantially faster than the streaming algorithm, and that GPUs add order-round speedups exceeding an order of magnitude over our multicore implementation on synthetic inputs with up to one billion temporal edges. On a 636M-edge Reddit network, eight GPUs compute 128 orders in 3.6 s of round time, whereas the streaming algorithm does not finish within an hour even at order 8.

cs.DS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09646

Fast and Actionable Classification of HPC I/O Time Series

Maxime Gonthier, Luan Teylo, Francesco Antici, Mihail Popov, Francieli Boito

原文摘要

Input/output (I/O) is a major bottleneck for highperformance computing (HPC) performance. One of the challenges is the contention between jobs when sharing the parallel file system. However, tackling this issue depends on knowing the temporal behavior of jobs. In this paper, we propose a clusteringbased approach to characterize jobs' temporal I/O behavior. We propose SAGE, a lightweight method that cuts I/O traces into short windows and learns an alphabet from them. The trace then becomes a sequence of symbols, which we call ''genome'', and we use clustering to separate them into classes. We evaluate SAGE and its two-stage variant SAGE-2 on 819,771 real traces from 2 HPC systems. We compare them against other techniques from the state of the art on three metrics, including one we propose. Compared to K-shape, the best baseline available, SAGE-2 lowers the median intra-cluster metrics by 9.1% on average, and achieves this in 2 minutes of clustering time versus 3.8 hours, a 109x speedup. More importantly, clusters created by SAGE and SAGE-2 carry enough information to act on: we evaluate the use of the cluster centroids for I/O scheduling, and show that our approach can decrease I/O stretch by up to 24% compared to the state of the art (round-robin placement).

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09644

A Separation Principle for Cooperative Multi-Agent Reinforcement Learning

Lucia Pezzetti, Nicolas Lanzetti, Antonio Terpin, Florian Dörfler, Giorgia Ramponi

原文摘要

We study cooperative multi-agent reinforcement learning (MARL) problems in which agents evolve under decoupled noisy dynamics but share a population objective. A canonical example is a mobility operator that orchestrates a fleet of vehicles to meet customer demand. The stochastic and time-varying nature of these problems calls for learning approaches, yet existing MARL algorithms do not scale: learning assignment and routing jointly over the full state-action space becomes prohibitive as the number of agents grows. Formulating the problem over the space of probability measures, we prove a separation principle: the population cost-to-go function is upper bounded by an optimal transport problem whose transportation cost is the target-conditioned single-agent cost-to-go function. This simplifies the multi-agent problem to learning a single-agent policy and coordinating the agents via optimal transport. Building on this principle, we introduce SALT (Separation-based Assignment and Learning via optimal Transport), a family of algorithms that learn a target-conditioned single-agent policy using any standard reinforcement learning algorithm, and then coordinate the agents via optimal transport. Since learning occurs at the single-agent level, SALT scales to fleet sizes where MARL baselines become intractable and transfers without retraining to fleet sizes unseen during training. In a vehicle-delivery experiment on the southern Manhattan road network, where MARL baselines are computationally prohibitive, SALT improves travel time by 15-21% over a shortest-path routing heuristic. In stochastic grid worlds, SALT reduces average travel times by 23-61% relative to MARL baselines and scales to population sizes where these methods fail.

cs.MA

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.38238

Variational Consequence-Driven Offline Reinforcement Learning

Wen Jiang, Ke Jiang, Jin Wang, Xiaoyang Tan

原文摘要

Offline reinforcement learning (RL) typically mitigates distribution shift by imposing divergence constraints on the action distributions of the learned and behavior policies. However, this paradigm cannot ensure the underlying dynamic consistency structure: even minor action deviations can lead to drastically different transitions, inadvertently driving the agent away from the offline dataset. To address this, we explore policy constraints through their induced state-consequence distributions rather than relying on pure action-distribution matching. Nevertheless, directly enforcing such consequence-aware constraints is computationally intractable without access to the true underlying dynamics model. To bypass this issue, we propose Structural Behavior Regularization (StBR), which replaces the intractable objective with a closed-form latent divergence. Structuring this latent space to reflect transition dynamics renders the latent divergence a theoretically bounded surrogate for the true consequence-distribution divergence. By regularizing the learned policy with this latent divergence, StBR adaptively tightens the behavior constraint in dynamics-sensitive regions, effectively preventing deviations outside the offline dataset. Empirically, we demonstrate that StBR achieves superior performance across diverse offline RL benchmarks.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09642

NAVE: A Normalized, Adaptive Conformer for Antarctic Whale Vocalization Detection

Matthias Nagl, Paul Primus, Gerhard Widmer

原文摘要

Antarctic whale vocalization detection requires temporally localizing call boundaries in long underwater recordings and classifying them into predefined call types. Training reliable machine learning models for this task is difficult due to the variability and sparsity of whale calls in passive acoustic monitoring datasets. These difficulties are further amplified by time-varying noise that can mask whale calls and by site-specific ambient sound characteristics to which a trained model may not generalize. In this work, we present NAVE, a Conformer-based detector that improves whale-call detection via three components. First, its input representation combines per-bin complex mean subtraction, which suppresses constant site-specific ambient sound characteristics, with an additional per-channel energy normalization (PCEN) channel that adapts to temporal variations in the noise floor. Second, a frequency-dynamic stem processes these noise-aware representations with convolutions that adapt across the characteristic frequency bands of the target call types. Third, we use a Conformer backbone with a convolution kernel matched to the duration of the target calls that adds an inductive bias and improves temporal localization. We evaluate NAVE on BioDCASE 2026 Task 2 using leave-one-site-out cross-validation across the validation sites, which approximates deployment on unseen recording sites. A single NAVE model with a tuned per-class threshold exceeds the stronger WhaleVAD-BPN baseline, using no boundary-proposal network and no search over post-processing parameters. A controlled ablation study shows that the normalized front-end and a duration-matched convolutional kernel each contribute substantially to recovering the D downsweep, the hardest class for previous detectors, with the wide kernel giving the largest aggregate gain.

eess.AS · cs.LG · cs.SD

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09640

LACART: Lookahead-Augmented Collision Avoidance and Reference Tracking Strategies for Safety-Critical Robots

Pedro Lopes, Amit Shivam, Luis Tiago Paiva

原文摘要

Autonomous racing provides a demanding testbed for collision avoidance under tight geometric and computational constraints. Building on a recent conference contribution and a master's dissertation, LACART combines lookahead-based control barrier functions (CBFs), a relaxed control Lyapunov function (CLF), and quadratic programming (QP) to modify nominal vehicle commands for obstacle avoidance and reference tracking. The method handles circular obstacles and track boundaries in a common optimisation layer. Comparative simulation results against Huang \emph{et al.} \cite{r9} show similar full-track paths and lower median computation times: 0.31 versus 0.33 ms per step and 0.023 versus 0.025 ms per QP solve. The dissertation further demonstrates a 1:43-scale vehicle with camera-based estimation and wireless actuation at a reported 25 Hz control rate. We identify the conditions linking lookahead-point clearance to body clearance, illustrate hardware limitations, and propose a recorded demonstration of safety intervention and recovery. The contribution targets ground-robot collision avoidance and real-time safety monitoring.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09636

Capacity of Generic Symmetric Distributed Linear Computation: The Geometry of Relations

Hua Sun

原文摘要

In distributed linear computation, $K$ users observe linear functions of common finite-field data and send separate messages to a server that wants another linear function. We characterize the generic capacity $C$, the minimum total number of field symbols sent per computed instance, when all observations and the demand have dimension $ν$, the data have dimension $μ$, and the coefficients of these linear functions are drawn independently and uniformly from the field and stay fixed during coding, with the field size growing through arbitrary prime powers. For fixed $K$, $C/ν$ depends only on $τ=μ/ν$: the curve has finitely many linear pieces for at most three users, infinitely many pieces approaching one value of $τ$ for four users, and a rational part between two sequences of linear pieces for five or more users. The geometry of the relation space, formed by the linear relations among the observations and the demand, determines three things: the shape of the curve, which relations the coding scheme aligns, and the entropy inequalities used in the converse. Generically, the user subspaces of this space are either balanced, where the capacity lies on a single rational curve, or split into two balanced components, a sparse and a dense one, which produces the linear pieces. Achievability uses asymptotic set alignment, in which monomial precoders confine the combined observation differences to a small set. The converse uses the same geometry to lower-bound the sum of the users' remaining observation entropies in terms of the remaining data entropy.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09634

Steerable Multi-Objective Group-Relative Preference Optimization with Jointly Trained Low-Rank Adapters

Constantinos Karouzos, Nikolaos Aletras

原文摘要

Language model deployment often requires different trade-offs between competing objectives as user preferences change, yet standard post-training methods typically produce a single policy for a fixed objective weighting. We introduce LambdaMix, a new parameter efficient method for learning a preference conditioned family of policies with group-relative online reinforcement learning. LambdaMix jointly trains one low-rank adapter per objective over a frozen base model. One preference vector selects the adapter mixture that generates responses and weights the separately normalized rewards used to update the adapters. A single training run supports changing preferences at inference without further optimization. We experiment with four models on helpfulness and harmlessness and on solving math problems while following instructions. We also evaluate the two largest models on rewriting comments to preserve wording while reducing clues about personal attributes. On the first two tasks, LambdaMix achieves higher hypervolume than all evaluated conditioned and policy merging baselines across all four models. LambdaMix uses fewer training completions and stores fewer adapter parameters than a set of GRPO specialist policies. With architecture and initialization fixed, varying adapter mixtures and objective weights together gives wider score ranges and higher coverage than varying either alone.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2409.20184

Adaptive Collision Sensitivity for Efficient and Safe Human-Robot Collaboration

Lukas Rustler, Matej Misar, Matej Hoffmann

原文摘要

What is considered safe for a robot operator during physical human-robot collaboration (HRC) is specified in corresponding HRC standards (e.g., ISO 10218-2:2025). The regime that allows collisions between the moving robot and the operator, called Power and Force Limiting (PFL), restricts the permissible contact forces. Using the same fixed contact thresholds on the entire robot surface results unnecessary productivity losses, as the robot needs to stop even when impact forces are within limits. Here we present a framework that decides whether the robot should interrupt motion based on estimated collision force computed individually for different parts of the robot body and dynamically on the fly, based on the Effective Mass (EM) of each robot link. We performed experiments on a simulated and real collaborative robot arm (UR10e) with sensitive skin (AIRSKIN) for collision detection and isolation. To demonstrate the generality of our method, we added experiments on simulated KUKA LBR iiwa robot, where collision sensing draws on joint torque sensing. On a mock pick-and-place scenario with both transient and quasi-static collisions, we show an increase in productivity over 45% from using the standard approach. The method is applicable to any robot for which the EF can be calculated.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09627

What remains of the planar induced minor conjectures?

Jadwiga Czyżewska, Tomáš Masařík, Marcin Pilipczuk, Paweł Rzążewski

原文摘要

Dallard, Milanič, and Štorgel, and independently Gartland and Lokshtanov, conjectured that for every planar graph $H$, Maximum Weight Independent Set and List $3$-Coloring are polynomial-time solvable in $H$-induced-minor-free graphs. As a structural explanation, Gartland and Lokshtanov further conjectured that such graphs admit balanced separators dominated by a constant number of vertices. Bonnet and Chang recently refuted the independent-set part of the algorithmic conjecture, by showing that Maximum Independent Set is $\mathsf{NP}$-hard in graphs excluding the $5\times5$ grid as an induced minor. We revisit both conjectures. We show that the graphs used by Bonnet and Chang exclude much simpler planar graphs, including several $16$-vertex trees, and we introduce a second, independent family of hard instances. We also prove that $3$-Coloring is $\mathsf{NP}$-hard in both settings, thus refuting the algorithmic conjecture in full, even in its quasipolynomial version, unless $\mathsf{NP}\subseteq\mathsf{QP}$. On the structural side, we identify planar graphs $H$ for which the structural conjecture holds, while the algorithmic one fails. Thus, constantly dominated balanced separators cannot by themselves explain the tractability of Maximum Weight Independent Set and List $3$-Coloring.

cs.DM · cs.CC · cs.DS · math.CO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09625

Encoding Memory in World Models with Test-Time Model-Based Reinforcement Learning

Maxime Burchi, Fayaz Ali Dharejo, Radu Timofte

原文摘要

Model-based reinforcement learning (MBRL) approaches have demonstrated strong performance across diverse domains in recent years, learning accurate world models from agent trajectories. However, despite this progress, current approaches still struggle to surpass human performance on tasks that require long-term memory and planning. In this work, we address the problem of long-term memory in MBRL by moving beyond the commonly studied paradigm of architecture design. We introduce Test-Time Model-Based Reinforcement Learning (TT-MBRL), using gradient descent at test time to encode the memory of episode observations into world model parameters. This enables the agent to learn a distinct world model for each episode, improving memory capabilities compared to traditional approaches that solely rely on contextual memory. We first establish a proof of concept on a simplified two-dimensional variant of the Memory Maze environment, which we refer to as Memory Maze 2D, showing consistent improvements across different world model architectures. We then demonstrate the effectiveness of TT-MBRL in the 3D domain, achieving new state-of-the-art results on the Memory Maze benchmark. Finally, we show that TT-MBRL maintains strong performance on non-memory benchmarks such as DeepMind Control and Atari 100k.

cs.LG · cs.AI · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09624

TPIC: Energy-Conserving Space Plasma Simulations on GPU Supercomputers

Luca Pennati, Ivy Peng, Stefano Markidis

原文摘要

Artificial heating and numerical damping affect particle energization in kinetic space plasma simulations, even when the numerical scheme remains stable. Implicit and semi implicit energy conserving Particle-in-Cell (PIC) methods eliminate these errors in the discrete energy balance while relaxing explicit stability constraints. We present TPIC, a massively parallel implementation of the Energy Conserving Semi-Implicit Method (ECSIM) for GPU supercomputers. We extend the exact energy conservation in the ECSIM formulation to closed domains with conducting boundaries by deriving and canceling a spurious term in the discrete Poynting flux theorem. TPIC executes the complete particle and field cycle on NVIDIA and AMD GPUs, combining matrix-engine acceleration with asynchronous communication and computation. We validate TPIC in a variety of electrostatic and electrodynamic test cases relevant for space plasma physics applications, including quasineutral plasma expansion, two-stream instability, Whistler waves, Weibel instability, Whistler turbulence, and three-dimensional magnetic reconnection with conducting walls and 12.3 billion particles. We show that in all scenarios, TPIC agrees with the corresponding analytical theory or reference numerical results, and preserves the total system energy to machine precision. On 4,096 AMD GPUs, TPIC advances 1.9 trillion particles with 96.8\% weak scaling efficiency.

cs.CE · cs.DC · cs.PF

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09623

Entering a New Domain in the Era of Generative AI: An Autoethnography of Solo Game Development as a Bioinformatics Educator

Hannah Kim

原文摘要

The rapid improvement of generative AI (GenAI) not only increased the efficiency of practice but also empowered individuals with capabilities to explore new domains. However, identity formation in the wake of such empowerment remains understudied. We use an autoethnography to understand identity formation in the context of solo game development as a bioinformatics educator. With the goal of developing a create-your-own-adventure (CYOA) game, we collected data for 73 days through journal entries, interacting with communities of practices. The autoethnographer had continuously reassessed the situation, and closely interacted with communities of practice. Their prior knowledge was used to provide in-game context and to uniquely interpret game development processes. These findings suggest that the autoethnographer made efforts to belong to communities of practice and engaged extensively in sensemaking through the lens of a bioinformatics educator. Although GenAI puts power at hand, attaining a new identity took place at a human pace.

cs.HC · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09622

On Positive Computation

Eduardo Skapinakis

原文摘要

We introduce new notions of reducibility for monotone classes and show that a monotone version of Circuit Satisfiability and the Hamiltonian Cycle Problem are complete for $mNP$, even under positive reductions (which are not the same as monotone reductions). If this holds for all (monotone) $mNP$-complete problems - that they are also so complete for $mNP$ -, then $P\neq NP$. We introduce positive models of relativized computation and show that the separation $posP\subsetneq mNP$ does not relativize, even when oracles are restricted to monotone sets. Yet, there is a (weak) diagonalization proof that $posP\subsetneq mNP$.

cs.CC · cs.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09621

BiLoGo: Reducing the smoothing of localised responses in Transformer-based neural operators for sheet forming

Yingxue Zhao, Haoran Li, Haosu Zhou, Tobias Pfaff, Nan Li

原文摘要

Trial-and-error FE simulation of sheet forming could be computationally expensive, neural-network-based learned simulators are used to accelerate design. These simulators should accurately capture localised responses, such as thinning around sharp radii or near ribs, which determine formability. Transformer-based neural operators propagate information efficiently across the mesh but tend to smooth such localised responses. This study develops BiLoGo, a Bipartite Local-Global learned simulator with an FE-guided design that connects local node-element message passing with a Transformer-based neural operator for long-range information exchange. BiLoGo has three modules: a bidirectional node-element GraphSAGE model that learns nodal-elemental coupling on a bipartite graph and predicts each quantity on its native FE entity without interpolation; a dual-stream Transformer-based neural operator that processes blank deformation and tool contact with separate token sets, preventing localised contact information from being averaged with the whole-blank state; and a fusion and element update module that combines global and local features via gated residual fusion and updates element features conditioned on element-node kinematics. Evaluated on a dome-shaped cold forming case and two corner-shaped hot stamping cases (one with ribs), BiLoGo achieves the lowest positional and threshold-based thinning errors on all three cases compared with single-level and hierarchical graph-based models and Transformer-based neural operators. Near the ribs, the smoothing index of predicted thinning increases from 0.41-0.56 for Transformer-based neural operators to 0.94 for BiLoGo-U. Compared with the hierarchical graph network CAtt-BiUGNN, BiLoGo-T reduces mean positional error by 15.5% on the Corner case and BiLoGo-U by 8.3% on the Corner+Rib case, with 66-69% less training memory.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09619

Beyond Feasibility: Efficient and Robust On-Device Localization with UWB Concurrent Transmissions

Enrico Soprana, Michael Stocker, Davide Vecchia, Matteo Trobinger, Carlo Alberto Boano, Gian Pietro Picco

原文摘要

Concurrent transmissions (CTX) enable an ultra-wideband (UWB) tag to range to, or self-localize from, multiple nodes with a single frame reception, significantly improving update rate and energy efficiency compared to traditional approaches. Prior work demonstrated the concept feasibility via prototypes whose real-world applicability is however hindered by many limitations, notably including their inability to directly execute on UWB tags and to cope with interfering UWB signals. We address these limitations in two ways. First, we redesign the core CTX techniques to enable a fast and efficient execution on resource-constrained MCUs without sacrificing accuracy. Second, we leverage the new features in the IEEE 802.15.4z standard to achieve robustness to interference from other UWB devices and enable tags to dynamically select which anchors to use, two crucial and unexplored capabilities in CTX-based systems. We quantitatively evaluate our approach experimentally in two real-world testbeds with different characteristics. We first analyze our new techniques individually, offering an in-depth discussion of design choices and performance trade-offs. Then, we embed all our techniques into a CTX-based system enabling a tag to self-localize entirely on-device, unlocking gains up to 44% in runtime and 85% in energy efficiency compared to traditional approaches.

eess.SP · cs.NI · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09617

CRTrack-Cap: A New Benchmark for Cross-View Dense Video Object Captioning

Xiangqun Zhang, Likai Wang, Zekun Qian, Rui Zhou, Ruize Han, Wei Feng

原文摘要

Dense video object captioning (DVOC) localizes, tracks, and describes object trajectories within a video. Existing methods process each camera view independently, producing separate camera-local tracklets and captions for the same person across cameras. These outputs lack a unified description and leave complementary appearance evidence across views unused. We therefore formulate cross-view DVOC (CVDVOC), which associates these tracklets into scene-level global trajectories and generates one caption for each trajectory from synchronized views. To support this task, we construct CRTrack-Cap from CRTrack by auditing text-identity links and converting usable referring expressions into canonical captions. It retains 425 global trajectories and 838 local tracklets across 13 scenes, with canonical captions for 335 identities. We adapt CHOTA to the cross-view setting as CV-CHOTA to jointly evaluate tracking and caption quality. Our reference framework combines View-Exclusive Cross-Camera Association (VECA) for grouping tracklets with Conflict-Evidence Gating (CEG) for integrating caption evidence using its reliability and disagreement across views. Against three single-view methods adapted with MvMHAT for cross-view association, our framework achieves CV-CHOTA scores of 83.43 and 33.48 on the scene-disjoint in-domain and cross-domain tests, respectively. Together, CRTrack-Cap and our framework provide a benchmark and a strong baseline for describing the same person consistently across camera views.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.25490

From Formulation to Articulation: Considerations for AI-Assisted Research in Information Theory

Chao Tian, Krishna Narayanan

原文摘要

Recent advances in artificial intelligence have contributed to several information-theoretic results through genuinely different modes: a language model may suggest a proof, participate in an extended mathematical dialogue, coordinate computation, or operate as one component of a search harness. Treating all of these as "AI-discovered mathematics" obscures distinctions that matter for scientific reliability, attribution, and reproducibility. This article examines a set of recent cases through a four-stage pipeline: how the problem was formulated; how AI participated in finding a solution; how the resulting analytical and computational claims were verified; and how machine-generated outputs were translated into mathematical exposition for human understanding. Based on these cases, we recommend that research publications (1) disclose the nature and extent of AI assistance at each stage, (2) report verification of analytical reasoning separately from certification of numerical computations, and (3) place more emphasis on articulating the underlying ideas, techniques, and interpretations.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09615

Contextual Search and Bilateral Trade with a Trace-Square-Root Potential

Romain Cosson, Federico Fusco, Anupam Gupta, Stefano Leonardi, Renato Paes Leme, Matteo Russo

原文摘要

We present a unified potential method for contextual search and bilateral trade with one-bit feedback and obtain order-tight algorithms for both problems. Our framework starts from a Bayesian perspective in which the unknown target (for contextual search) and the unknown pair of valuations (for bilateral trade) are sampled from a known prior distribution. In that model, the decision maker bases its prediction on the posterior distribution, obtained by conditioning on feedback received in previous rounds. For Bayesian contextual search, we present an algorithm with a total loss bounded by $O(\mathrm{Tr} \sqrt Σ)$, where $Σ$ is the covariance matrix of the target's prior; and for contextual bilateral trade, our algorithm's regret is bounded by $O(\mathrm{Tr} \sqrt{Σ_S}+\mathrm{Tr} \sqrt{Σ_B})$, where $Σ_S$ and $Σ_B$ are the covariance matrices of the seller's and buyer's valuations. We then use ideas from this Bayesian setting and adapt a ``dual'' of this trace-square-root potential to the classical worst-case setting, where the target and valuations are chosen adversarially from the unit Euclidean ball. For both contextual search and bilateral trade, we obtain tight $O(\sqrt d)$-regret algorithms. These results match the existing lower bounds up to a constant factor and improve on the previous guarantees of $O(d\log d)$ for contextual search and of $O(d6^d)$ for bilateral trade.

cs.GT · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09614

Know What You Need: Efficient Activation Checkpointing

Danyal Ahmed, Edouard Oyallon

原文摘要

Large Language Model training is increasingly constrained by memory capacity. Activation Checkpointing reduces activation memory by avoiding storing selected activations during the forward pass and rematerializing them during backpropagation, thereby trading memory savings for additional computation. Although this technique is essential for scaling model training, rematerialization can be prohibitively expensive, particularly for Transformer linear layers. Approximate computation could reduce this overhead, but conventional approaches either introduce errors that accumulate over the backward pass or fail to exploit information available from the initial forward pass. We introduce a novel approximate rematerialization method that can yield unbiased gradient estimates: using information collected during the initial forward pass, the method selectively recomputes only the most important layer components, either through greedy selection or randomized sketching. Ablation studies show that the resulting gradients closely match the exact gradients. When applied to feed-forward layers, attention layers, or both, our method can recompute only two-thirds of the layer components in a Llama-488M model, while increasing perplexity by just 1.7%, 1.2%, and 2.5%, respectively.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09612

Kernel-Based Learning of Analytic Operators and Parametric PDEs

Giovanni S. Alberti, Markus Holzleitner, Lorenzo Rosasco, Matteo Santacesaria

原文摘要

We study kernel-based learning of analytic operators from finite samples, with a focus on how the regularity of the target should determine the choice of the kernel. We establish embeddings of analytic operators into vector-valued reproducing kernel Hilbert spaces and derive finite-sample, high-probability guarantees that quantify the uncertainty due to random sampling: in the noiseless setting, suitable analytic kernels yield rates exponential in a fractional power of the sample size, while Sobolev-type kernels yield polynomial rates. We apply the theory to the solution operator of a parametric elliptic PDE, where the training data are themselves finite-element approximations, and obtain bounds that separate the sampling and discretization contributions to the error and prescribe how the mesh size should be coupled to the sample size. The results are illustrated numerically.

stat.ML · cs.LG · math.ST

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新论文 · v1arXiv:2611.09611

Online Timely Bin Packing Revisited: A Simpler Algorithm with Optimality in Special Cases

Pei-Lin Chiang, Wing-Kai Hon, Ying-Chia Huang, Li-Tzu Yang

原文摘要

Online Timely Bin Packing is a variant of classical online bin packing in which items arrive over time with sizes and deadlines. Packing an item may be deferred until its deadline, but once a collection of items is packed into a bin, the bin is closed and cannot be used again. The objective is to minimize the total number of bins used. The problem was introduced by Khattar in 2023. In 2025, Aminian proposed Discrete-Greedy, a deterministic online algorithm with a competitive ratio of exactly $2$, which remains the best-known upper bound. Its analysis is particularly simple; however, the algorithm requires maintaining a fractional simulation internally and may leave usable space in some bins unfilled. We propose Thrifty-Greedy, a simple deterministic online algorithm that does not require a fractional simulation. We prove that its competitive ratio is exactly $2$, matching the best-known upper bound. At the same time, the algorithm automatically achieves the optimal competitive ratio on two natural classes of inputs: it is $1$-competitive when all items have the same size, and $1.5$-competitive when every item has size in $\{1/3,2/3\}$. In contrast, Discrete-Greedy has a competitive ratio of at least $2$ on both classes.

cs.DS

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版本更新 · v2arXiv:1303.7276

The effect of limb kinematics on the speed of a legged robot on granular media

Chen Li, Paul B. Umbanhowar, Haldun Komsuoglu, Daniel I. Goldman

原文摘要

Achieving effective locomotion on diverse terrestrial substrates can require subtle changes of limb kinematics. Biologically inspired legged robots (physical models of organisms) have shown impressive mobility on hard ground but suffer performance loss on unconsolidated granular materials like sand. Because comprehensive limb-ground interaction models are lacking, optimal gaits on complex yielding terrain have been determined empirically. To develop predictive models for legged devices and to provide hypotheses for biological locomotors, we systematically study the performance of SandBot, a small legged robot, on granular media as a function of gait parameters. High performance occurs only in a small region of parameter space. A previously introduced kinematic model of the robot combined with a new anisotropic granular penetration force law predicts the speed. Performance on granular media is maximized when gait parameters minimize body acceleration and limb interference, and utilize solidification features of granular media.

physics.bio-ph · cond-mat.soft · cs.RO · physics.flu-dyn · q-bio.QM

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新论文 · v1arXiv:2611.09610

New persistent chain spaces: identifying and computing the relevant persistent Laplacians of a filtration

Joris Kirchner, Martina Vittorietti

原文摘要

Persistent homology (PH) summarizes the topological features of data that persist across scales, but it is insensitive to many geometric properties. Persistent Laplacians (PLs) address this limitation: for a pair of simplicial complexes in a filtration, the kernel of the persistent Laplacian recovers the persistent Betti number, while its non-zero spectrum carries additional geometric information. However, PLs are defined for individual pairs of filtration values, so analysing a whole filtration requires evaluating many operators that largely contain the same information, and the result is hard to summarize and interpret. In this paper, we show that this redundancy has a precise structure. For each pair of filtration values, we decompose the space of chains underlying the persistent up-Laplacian into the contributions inherited from the neighboring pairs and a remaining part, which we call the new persistent chain space. We show that the new persistent chain spaces of all earlier pairs form a direct sum decomposition of this space, so that every persistent up-Laplacian in the filtration is determined by them. Moreover, the new persistent chain spaces are non-trivial only at pairs given by the persistence pairing in the same and in the next homological dimension, so that the persistence pairing determines exactly where the persistent up-Laplacian can change. Experiments on several types of point clouds show that evaluating persistent Laplacians only at these pairs reduces the computation time, both for our implementation and for existing software.

math.AT · cs.CG · math.CO · math.SP

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新论文 · v1arXiv:2611.09609

Not Your Diary, Just Your Topic: Grading the Wrong-User Control

Karina Romanova, Igor Kiselev, Vladimir Platonov, Viliana Devbunova, Gregory Leleytner, Adeliya Leleytner, Lina Veltman, Aleksandr Nikolich

原文摘要

A common test of personalization replaces a user's history with someone else's and measures the drop in accuracy. But this swap also changes the topics in the history, so the drop may reflect topic mismatch. We examine this problem with a frozen language model on LaMP-2 news categorization, using histories from similar users and synthetic histories assembled from other users' articles. The user's own history improves accuracy by 2.3 percentage points over a model trained without history. Removing history only at evaluation makes this gain appear larger, at 4.8 points. Category labels alone reproduce 86% of the accuracy gap between the user's own history and a random user's history. With synthetic histories matched on labels and text similarity, the gap falls to 0.9 points. Across two user splits and three training seeds, the advantage over a similar user's history is 3.2 points for users seen during training and 0.65 points for unseen users, where the evidence for an advantage remains inconclusive. A small movie-tagging experiment with one training seed also finds no advantage over other users' histories. These findings show why personalization should be evaluated on unseen users, with histories of similar content and a model trained without history as comparisons. We also find that adding gated cross-attention improves on a prefix-only model under our training setup; whether this holds more broadly remains open.

cs.AI

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版本更新 · v2arXiv:2502.17175

Linear Bandits on Ellipsoids: Minimax Optimal Algorithms

Raymond Zhang, Hedi Hadiji, Richard Combes

原文摘要

We consider linear stochastic bandits where the set of actions is an ellipsoid. We provide the first known minimax optimal algorithm for this problem. We first derive a novel information-theoretic lower bound on the regret of any algorithm, which must be at least $Ω(\min(d σ\sqrt{T} + d \|θ\|_{A}, \|θ\|_{A} T))$ where $d$ is the dimension, $T$ the time horizon, $σ^2$ the noise variance, $A$ a matrix defining the set of actions and $θ$ the vector of unknown parameters. We then provide an algorithm whose regret matches this bound to a multiplicative universal constant. The algorithm is non-classical in the sense that it is not optimistic, and it is not a sampling algorithm. The main idea is to combine a novel sequential procedure to estimate $\|θ\|$, followed by an explore-and-commit strategy informed by this estimate. The algorithm is highly computationally efficient, and a run requires only time $O(dT + d^2 \log(T/d) + d^3)$ and memory $O(d^2)$, in contrast with known optimistic algorithms, which are not implementable in polynomial time. We go beyond minimax optimality and show that our algorithm is locally asymptotically minimax optimal, a much stronger notion of optimality. We further provide numerical experiments to illustrate our theoretical findings.

stat.ML · cs.LG

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版本更新 · v2arXiv:2611.07393

CacheGraft: Enhancing Retrieval-Augmented Generation via Latent-Space Document Enrichment

Vihan Lakshman, Peter Baile Chen, Zhuohan Gu, Michael Cafarella

原文摘要

Recent work in machine learning has demonstrated that transformer-based language model agents can communicate state not only through text, but also directly in latent space via key-value (KV) caches. In this paper, we argue that such cache-to-cache translation can provide utility beyond the original motivating application of real-time agent communication and can instead be utilized for retrieval-augmented generation (RAG). Building on the Cache-to-Cache (C2C) fusion framework, our proposed system CacheGraft encodes each corpus passage offline into a fused KV cache representation between a larger sharer model (4B parameters in our experiments) and a smaller receiver model (0.6B parameters) such that this cache can be injected into the receiver at query time with no need for the larger model. Across five QA benchmarks and two independent locally-run judges, CacheGraft improves answer quality over the text-only receiver baseline in 29 out of 30 comparisons, of which 17 of the 30 are statistically significant. Notably, these gains are zero-shot with respect to the fuser, which was never trained on retrieval data. These initial results suggest that latent-space document enrichment may be a promising avenue for enhancing the performance of RAG systems, particularly in compute-constrained settings where it is only feasible to deploy a small model at inference time.

cs.IR · cs.LG

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新论文 · v1arXiv:2611.09607

ReplayAudit: Auditing Outsourced LLM Fine-Tuning via Sealed Records and Selective Replay

Yihan Xia, Wenxin Xu, Taotao Wang

原文摘要

Auditing outsourced LLM fine-tuning requires checking an untrusted provider's training while controlling verification cost. Uniform sampling ignores training-time signals, while checking a suspicious record can require replaying several preceding steps. We present ReplayAudit, a telemetry-guided auditing system that seals training records before challenges and independently replays sampled intervals from sealed checkpoints. Loss changes and parameter-update norms guide stratified random sampling; replay determines whether the selected computation agrees with its records. We relate sampling probabilities to replay work and evaluate the system on real LoRA training. A 7B BF16 prototype rejects a false update-norm report during a random audit; targeted checks also reject false loss reports and skipped updates. We calibrate sampling budgets offline to at least a 90% probability of directly selecting an injected step. For two learning-rate deviation settings, executing these budgets on the same honest 1.5B trajectory reduces median complete-audit wall time by 49% and 67% relative to uniform sampling. When skipped updates evade the high-anomaly stratum, the corresponding audit instead costs 38% more. These results show how deviation capture and actual replay work jointly determine the cost of telemetry-guided auditing.

cs.CR · cs.LG

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新论文 · v1arXiv:2611.09605

DKWBC: Decoupling Motion Tracking and Balancing for Humanoid Whole-Body Control

Dingkun Wei, Zehong Shen, Huaijin Pi, Xueyang Wang, Xing Zhu, Hujun Bao, Yujun Shen, Xiaowei Zhou

原文摘要

Humanoid whole-body control requires a robot to track diverse reference motions while maintaining balance under gravity, torque limits, contact switching, and external disturbances. Existing reinforcement-learning-based trackers typically learn control outputs that jointly address motion tracking and balance. However, similar reference poses can correspond to substantially different dynamic demands, requiring a unified policy to learn motion-dependent control requirements alongside balance corrections. This can complicate learning and lead to high-frequency fluctuations in control outputs. We propose DKWBC, a dynamics-informed whole-body control framework that combines explicit model-based tracking with a learned balancing residual. Inspired by the model-based and environmental-wrench contributions to joint torques in floating-base dynamics, DKWBC uses inverse-dynamics feedforward and joint PD feedback to supply tracking torques, while a residual policy learns additional joint-torque corrections for balanced execution. By explicitly accounting for motion-dependent torque demands, this structure simplifies the policy's learning task. Experiments in simulation and on real hardware demonstrate that a single learned balancing policy generalizes across motion datasets while maintaining accurate motion tracking and balance across ground support, attached payloads, and physical interaction. Project page: https://robbyant-research.github.io/DKWBC/.

cs.RO

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