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版本更新 · v3arXiv:2510.04606

Closed-Form Last Layer Optimization

Alexandre Galashov, Nathaël Da Costa, Liyuan Xu, Philipp Hennig, Arthur Gretton

原文摘要

Neural networks are typically optimized with variants of stochastic gradient descent. Under a squared loss, however, the optimal solution to the linear last layer weights is known in closed-form. We propose to leverage this during optimization, treating the last layer as a function of the backbone parameters, and optimizing solely for these parameters. We show this is equivalent to alternating between gradient descent steps on the backbone and closed-form updates on the last layer. We adapt the method for the setting of stochastic gradient descent, by trading off the loss on the current batch against the accumulated information from previous batches. We provide theoretical analyses showing convergence of the method to an optimal solution in the neural tangent kernel regime, as well as quantifying the gains compared to standard SGD in a one-step analysis. Finally, we demonstrate the effectiveness of our approach compared with SGD and Adam on a squared loss in several regression tasks, including neural operators and causal inference.

cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09693

Real-Time Self-Penetration Repair for Skinned Characters with Soft Contact Budgets

Yifei Zhu

原文摘要

Motion that is plausible on one body can penetrate another after a change in shape, and motion models can produce self-penetration on their native characters. We present a training-free post-process that reduces these artifacts through pose correction followed by bounded skin compression. The module attaches to any motion model whose native character and output character have compatible character packs, without access to model internals. Each pack is built offline from the rig and skin without training. A vertex-level pose layer solves a local quadratic program in plain C on one CPU core, and a stateless contact deformer acts on the host's final skinned mesh. A part-dependent soft-tissue budget allocates correction between pose and skin through a static convention. Source penetration contributes no extra allowance; optional cloth thickness and compliance modify the pose layer's collision surface. Operation is causal or uses a short buffered lookahead, with frame-exact pass-through at the native interface for clean, inactive frames in the zero state. Across 320 unseen clips and 3 body shapes, the fraction of frames with hand-torso depth above 20 mm falls from 15.53% for copied motion to 0.00%, with final maximum depth 9.3 mm over every frame. Mean pose and skin costs are 1.11 and 1.18 ms, respectively, and their sum of 2.29 ms is a sub-frame processing cost. The median joint-velocity high-frequency ratio relative to copied target motion is 1.008.

cs.GR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09692

PoseRieFlow: Riemannian Flow Matching for Whole-Body Text-to-Pose Generation

Jan Spindler, Bardia Ardakanian, Patrick Stotko, Reinhard Klein

原文摘要

We present PoseRieFlow, a compact flow-matching framework for text-conditioned generation of whole-body 3D human poses. Prior text-to-pose methods either neglect articulated hands or model rotations in Euclidean space despite their non-Euclidean geometry. PoseRieFlow instead formulates generation directly on the product manifold of SMPL-H joint rotations, learning a text-conditioned vector field that transports samples from an isotropic $\mathrm{SO}(3)$ Gaussian prior to the data distribution along Riemannian geodesics. A part-tokenized Diffusion Transformer aggregates the 52 joints into eight body-part tokens, allocating a dedicated token and nonlinear projector to each hand, and restores per-joint resolution through part-specific decoders, shortening the sequence while improving kinematic fidelity over per-joint tokenization. With only 21.6M trainable parameters, PoseRieFlow supports expressive SMPL-H generation with detailed hand articulation at a fraction of the scale of recent LLM-based generators. On PoseScript, it attains the lowest whole-body kinematic error among all hand-producing methods, with consistent kinematic gains from the Riemannian formulation over its Euclidean counterpart at every practical inference budget, while distributional and retrieval metrics remain comparable between them.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2607.21551

Unconditional Unclonable Encryption

Prabhanjan Ananth, Seyoon Ragavan, Amit Sahai

原文摘要

We give an unconditional construction of information-theoretically secure one-time private-key unclonable encryption for one-bit messages, with efficient encryption and decryption and exponentially small unclonable-indistinguishability advantage. Previous work by Bhattacharyya and Culf (Nature Physics, 2026) and Bhattacharyya, Broadbent, and Culf (arXiv:2603.08916) either only showed inverse-polynomial security loss or required inefficient encryption/decryption operations; we avoid both of these caveats. Our scheme's key is a uniformly random non-identity phase-free Pauli on $n$ qubits, and bit $a$ is encrypted as a random $(-1)^a$ eigenstate of that Pauli. We prove that the probability that both receivers recover the bit is at most $1/2 + O\left(2^{-n/2}\right).$ By a lower bound due to Broadbent, Culf, and Rochette (IACR Communications in Cryptology, 2026), this is the best probability bound achievable with $n$-qubit ciphertexts while ensuring perfect correctness (up to the constant hidden in the $O(\cdot)$).

quant-ph · cs.CR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.20122

Optimizing Computational-Statistical Runtime for Wasserstein Distance Estimation

Peter Matthew Jacobs, Jeff M. Phillips

原文摘要

Squared Wasserstein distance is a frequently used tool to measure discrepancy between probability distributions. This distance is typically computed between empirical measures of size $n$ from two underlying random samples. Unfortunately, even in lower dimensional Euclidean space problems $\left( d \in \{2,3\} \right)$, algorithms for Wasserstein distance computation with approximate or exact precision guarantees scale poorly in the runtime as a function of $n$ and the desired precision. In response, we consider the computational-statistical runtime, where the goal is to estimate from samples the Wasserstein distance between potentially smooth measures up to $ε$-additive error in expectation with respect to the sampling; we allow $O(1)$ computational cost for collecting a sample. Towards this, we develop a Sample-Sketch-Solve paradigm where we introduce a regular cartesian grid sketch of the samples. We show that (especially under $α$-Hölder smooth distributions) this can compress the data without increasing asymptotic error, and also regularizes the structure which enables faster exact algorithms. Ultimately, we approximate $W_2^2(P,Q)$ within $ε$ error in $ε^{-\max(2,\frac{d+1+o(1)}{1+α})}$ time for $0 < α< 1$ Hölder smooth distributions $P,Q$ on $(0,1)^{d}$; an optimal $Θ(ε^{-2})$ for $α> 1/2$ when $d=2$ and nearly optimal as $α\to 1$ when $d = 3$.

stat.ML · cs.CC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.17102

Knowledge Localization in Mixture-of-Experts LLMs Using Cross-Lingual Inconsistency

Lucas Bandarkar, Alan Ansell, Trevor Cohn

原文摘要

Modern LLMs continue to exhibit significant variance in behavior across languages, such as being able to recall factual information in some languages but not others. While typically studied as a problem to be mitigated, in this work, we propose leveraging this cross-lingual inconsistency as a tool for interpretability in mixture-of-experts (MoE) LLMs. Our knowledge localization framework contrasts routing for sets of languages where the model correctly recalls information from languages where it fails. This allows us to isolate model components that play a functional role in answering about a piece of knowledge. Our method proceeds in two stages: (1) querying the model with difficult factual questions across a diverse set of languages to generate "success" and "failure" activation buckets and then (2) applying a statistical contrastive analysis to the MoE router logits to identify experts important for knowledge. To validate the necessity of this small number of experts for answering a knowledge question, we deactivate them and re-ask the question. We find that despite only deactivating about 20 out of 6000 experts, the model no longer answers correctly in over 40% of cases. Generally, this method provides a realistic and scalable knowledge localization approach to address increasingly complex LLMs.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.02474

MemSkill: Learning and Evolving Memory Skills for Self-Evolving Agents

Haozhen Zhang, Quanyu Long, Jianzhu Bao, Tao Feng, Weizhi Zhang, Haodong Yue, Wenya Wang

原文摘要

Most Large Language Model (LLM) agent memory systems rely on a small set of static, hand-designed operations for extracting memory. These fixed procedures hard-code human priors about what to store and how to revise memory, making them rigid under diverse interaction patterns and inefficient on long histories. To this end, we present \textbf{MemSkill}, which reframes these operations as learnable and evolvable memory skills, structured and reusable routines for extracting, consolidating, and pruning information from interaction traces. Inspired by the design philosophy of agent skills, MemSkill employs a \emph{controller} that learns to select a small set of relevant skills, paired with an LLM-based \emph{executor} that produces skill-guided memories. Beyond learning skill selection, MemSkill introduces a \emph{designer} that periodically reviews hard cases where selected skills yield incorrect or incomplete memories, and evolves the skill set by proposing refinements and new skills. Together, MemSkill forms a closed-loop procedure that improves both the skill-selection policy and the skill set itself. Experiments on LoCoMo, LongMemEval, HotpotQA, and ALFWorld demonstrate that MemSkill improves task performance over strong baselines and generalizes well across settings. Further analyses shed light on how skills evolve, offering insights toward more adaptive, self-evolving memory management for LLM agents. Code is available at https://github.com/ViktorAxelsen/MemSkill

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2605.09319

PGID: Progressive Guided Inversion and Denoising for Robust Watermark Detection

Minh Quoc Duong, Chun Tong Lei, Chun Pong Lau

原文摘要

With the proliferation of AI-generated images, digital watermarking has become an essential safeguard for protecting intellectual property and mitigating malicious exploitation. Recent works on semantic watermarking have enabled efficient copyright protection for diffusion models. However, the dependence of semantic watermarking on diffusion inversion for watermark detection creates a critical vulnerability. Imprint removal and forgery attacks exploit this weakness to produce deceptive results. Our analysis reveals that these attacks succeed by displacing watermarked latents into the unwatermarked region, while guiding unwatermarked latents into the watermarked region. Based on that, we propose Progressive Guided Inversion and Denoising (PGID), the first plug-and-play, training-free noise extraction framework designed to defend against both attack strategies. PGID effectively defends by projecting perturbed latents back to the region where they originally belong. The projection is achieved by eliminating intermediate latent deflections and mitigating adversarial perturbations through progressive inversion-denoising cycles. Comprehensive evaluations across multiple schemes demonstrate that PGID successfully restores detection reliability by recovering removed watermarks and identifying forged instances.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09682

The Multiscale Single-Index Model: An MSE Analysis of Online SGD Dynamics

Gordon Dai, Joan Bruna

原文摘要

We consider the Multiscale Single-Index Model (MSIM), first introduced by Oymak and Soltanolkotabi (2021), as a stylized model for hierarchical feature learning with scale separation. Each layer extracts a shared single-index feature at one physical scale and passes it to the next, giving a tractable setting in which to study how deep architectures learn local multiscale representations. Under non-degeneracy and delocalization assumptions on the link function and planted features respectively, for the two hidden-layer case ($K=2$), we analyze joint online spherical SGD under MSE loss, which is a canonical regression objective, in discrete time and show that it achieves weak recovery from random initialization in $O(d\log^2 d)$ samples, followed by strong recovery to accuracy $\varepsilon$ in $O(d\log(1/\varepsilon))$ additional samples. The main technical challenge arises from the non-Gaussian learned features observed by the second layer. We address this challenge through a quantitative Gaussian comparison using delocalization, combined with sharp trajectory-level control of the coupled SGD dynamics. This work is a companion to Bruna (2026, arXiv:2607.03347), which studies recovery under a correlation objective for general $K$.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09680

SPARSEUP: Towards Better Recipes for Training Modern Learned Sparse Retrievers

Thibault Formal, Pierre Erbacher, Cosimo Rulli

原文摘要

Learned sparse embeddings are a powerful alternative to dense retrieval, yet they have fallen behind single-vector and late interaction models. One reason for their limited adoption is that they are notoriously difficult to train, generally requiring very tight conditions to converge. We present a recipe for training Splade-like encoders on modern backbones and data that achieves a better efficiency-effectiveness trade-off. We discuss the role of logit distribution and its impact on the training stability and the final sparsity of the encoder. We also introduce a new per-position fan-out control - a hard cap on how many vocabulary dimensions each position may emit. We further apply a simple vocabulary merging strategy that removes redundant entries - such as case and word-boundary variants of the same word - which are common in cased BPE-based backbones. We apply our recipe to the LateOn backbone and training data, obtaining SPARSEUP, a sparse encoder that reaches 56.4 average nDCG@10 on BEIR-13, the highest we know of among public vocabulary-based sparse encoders below 150M parameters.

cs.IR

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09679

Autonomous Particle Image Velocimetry Using Zero-Shot Sim-to-Real Reinforcement Learning

Chang Rajani, Luke Sebastien Marsden, Stephan Kunz, Claudio Mucignat, Ivan Lunati

原文摘要

Particle image velocimetry (PIV) reconstructs a flow field from the motion of tracer particles but the reconstruction quality is bounded by how well the flow structure of interest is resolved in the images. The acquisition position is typically fixed by the operator before the measurement. We cast camera placement as a sequential decision-making problem and train a reinforcement learning agent to control a 6-DoF manipulator equipped with a camera, enabling it to locate, frame and resolve a hydrofoil wake in a water tunnel. The agent commands the camera pose and observes the scene only through the velocity field and uncertainty estimated from time-separated image pairs. It is trained entirely in a photorealistic simulation of our water tunnel, with wakes generated by Computational Fluid Dynamics (CFD) simulations of hydrofoils with varying profiles, positions and angles of attack. After zero-shot transfer to the real water tunnel, the policy reliably locates and frames the wakes of randomly placed hydrofoils. It resolves the wake at a higher resolution than a fixed camera view and with lower error in the measured flow statistics, in roughly half the time of a full scan and with far fewer image pairs.

cs.RO · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09678

Calibrating demand models versus adjusting OD matrices with traffic counts: a controlled comparison

Yue Li, Shujuan Chen, Ying Jin

原文摘要

Traffic counts are routinely used to calibrate travel demand models, particularly when the origin-destination (OD) matrix is not observed directly. An important modelling decision is whether to adjust individual OD flows or the parameters of the demand model that generates them. The latter approach uses fewer parameters and preserves a relationship between zonal characteristics and demand, but its accuracy depends on the assumed model structure. We compare three established approaches using the same prior information, traffic counts and equilibrium assignment. They estimate five production-gravity coefficients, origin and destination adjustment factors, or individual OD flows. Synthetic experiments on the Eastern Massachusetts and Chicago Sketch networks allow recovery to be assessed against known demand under different forms of model misspecification. Calibrating the five-coefficient model gives better OD recovery and held-out link-flow prediction when demand follows its assumptions or a two-purpose gravity process. Its advantage declines or reverses when destination choice or production relationships vary across zones. Cellwise adjustment achieves the closest fit to calibration counts in 65 of 66 evaluated conditions, yet it recovers OD demand least accurately of the three approaches in 45 of them. These findings show that model parameterisation matters beyond calibration fit. For transport planning, traffic counts should be used alongside independent evidence on trip production and distribution when selecting and validating a demand model.

cs.CE · stat.ME

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.05660

AQUA - A Modular Authentication Quality Modeling Framework

Alex R. Mattukat, Michael Zerbe, Krisa Carka

原文摘要

Authentication solutions are the key design, architectural, and implementation decisions that implement authentication security controls. An example is a password-based authentication solution. While researchers often examine authentication quality, existing approaches do not distinguish which effects on quality stem from which aspect of authentication. For example, multi-factor authentication solutions tend to be less usable because they combine multiple authenticators, whereas passkeys are more secure than passwords because of properties inherent to the authenticator itself. This paper presents AQUA, a modular quality modeling framework for authentication solutions. AQUA's core idea is to decompose authentication into four interrelated concepts, enabling modular specification of authentication quality and tracing quality effects back to their source. We demonstrate AQUA with initial definitional quality models that address the levels of authenticators, authentication techniques, and authentication solutions. We further illustrate the interrelations between these concepts through concrete examples and a usability metric, showing how quality propagates across the concepts and ultimately affects the overall quality of an authentication solution. Our results provide a foundation for future work, including designing assessment and prediction models for authentication solutions and automated quality assessments.

cs.CR · cs.SE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.28065

OperatorSHAP: Fast and Accurate Shapley Value Estimation for Neural Operators

Joshua Stiller, Santo M. A. R. Thies, Felix Czaja, Eyke Hüllermeier

原文摘要

Understanding model predictions is essential for physical applications, where outputs often inform safety-critical decisions, such as structural load assessment, weather warnings, and clinical diagnosis. Shapley values satisfy many desirable properties as an attribution method, but their computational cost during inference hinders their practical use. Current amortized explainers, such as FastSHAP, are limited to homogeneous inputs, which is problematic for physical applications where data often comes from irregular grids and geometries. We introduce OperatorSHAP, a grid-agnostic attribution method that allows us to train FastSHAP-like explainers for neural operators. We establish a theoretical framework for attributions in function space, connecting to Aumann-Shapley values. We further show that OperatorSHAP's explanations are consistent with state-of-the-art discrete Shapley values across resolutions and transfer across grid sizes without retraining.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.10772

Outgrowing the Curse of Multilinguality: Scaling Strategies for Efficient Language Adaptation

Kevin Glocker, Kätriin Kukk, Romina Oji, Marcel Bollmann, Marco Kuhlmann, Jenny Kunz

原文摘要

Training high-performing language models which include medium- and lower-resource languages remains a challenge. Massively multilingual models still underperform language-specific adaptations, especially at smaller scales, due to the curse of multilinguality. In this work, we investigate scaling as an efficient strategy for adapting pretrained models to new target languages. Through comprehensive ablations matched for data and training FLOPs, we test whether upscaling from English base models enables more effective and resource-efficient adaptation than standard continued pretraining. We find that upscaling is an efficient strategy for improving target-language performance by relieving capacity constraints, especially for higher-resourced languages. An initial upscaling step in English is beneficial for low-resource languages. However, in higher-resource scenarios it is more compute-efficient to directly scale up on the target language, albeit slightly compromising data efficiency for most languages. For multilingual continued pre-training, we identify directly scaling up using the target data as the optimal setup for both data efficiency and reduced English forgetting.

cs.CL · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.26090

Finding and using interpretable latents in a neutrino foundation model with sparse autoencoders

Raphaël Bonnet-Guerrini, Johann Ioannou-Nikolaides, Inar Timiryasov, Vincenzo Piuri

原文摘要

We present a first application of sparse-autoencoder-based mechanistic interpretability to particle physics. Studying a neutrino foundation model pretrained on IceCube data and fine-tuned for direction reconstruction, we identify a validated atlas of physical concepts in the model representation, using a strict validation protocol consisting of held-out tests, matched nuisance controls, and replication across independent dictionary trainings. Causal interventions show that the direction head barely draws on this atlas. Motivated by this underused information, we train an uncertainty head on the same event-level representation to predict the model's angular reconstruction error. Unlike the direction head, it depends causally on quality and brightness features from the atlas. These results suggest that mechanistic interpretability can reveal learned latent physics encoded within a model's internal representation and help design downstream tasks that exploit it.

astro-ph.HE · cs.AI · cs.LG · hep-ex

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09665

Online Causal Configuration for Networked Systems via Doubly Robust Steady-State Learning

Yuli Liu, Zhiheng Zhang

原文摘要

Measurement exploration reveals how interacting local controllers behave but can alter the steady-state regime a network operator seeks to optimize. Exploratory rewards may therefore fail to identify a configuration's no-exploration value: under full exploration, identical logged distributions can conceal different target values. Identification requires accounting for both action-law mismatch and shifts in the steady-state context distribution. We introduce CAUSAL-UCB, which selects configurations using optimistic bounds on target causal values. Its interference-aware clipped doubly robust evaluator produces predecision certificates separating statistical variation from calibrated identification, transport, and temporal-dependence errors. For any finite horizon and predetermined schedule of measurement-exploration rates $\{η_t\}$, we prove lower bounds of $Ω((1+\sum_tη_t)^{-1/2})$ on minimax inference error and $Ω(\sum_tη_t)$ on measurement-induced deployment loss in a stationary, potentially slow-mixing family. We bound regret under valid certificates and selected-information growth, and establish an anytime finite-sample certificate with a shared radius for estimation, selection, and reporting in a restricted known-local-interference model. In a high-mismatch benchmark with 24 paired seeds and 200 rounds, CAUSAL-UCB attains target regret of $1.732$ versus $6.326$ for certified IPS-UCB, with deployment gaps of $30.607$ and $23.217$, respectively. Independent-audit coverage tests and trace-driven semi-synthetic studies further assess certificate coverage and deployment trade-offs.

cs.LG · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09664

Dual Architecture Fusion Vehicle Lidar 3D Target Detection

Muyun Li, Weizheng Jin, Yuan Sun

原文摘要

In this paper, a 3D backbone network (SpconvTransformer) based on the fusion of convolutional architecture branch and Transformer architecture branch is proposed. The convolution branch uses 3D sparse convolution and submanifold convolution to learn local voxel features and semantic information, and the Transformer branch uses the self-attention mechanism to model the remote relationship between voxels, uses the broad receptive field to capture rich context information, and complementing the advantages of the two architectures by fusing the local features extracted from the two branches and the global features solves the problem of limited receptive fields of 3D convolutional neural networks. The experimental results show that the average accuracy of SpconvTransformer in 3D object detection and bird's-eye view detection tasks reaches 81.45% and 88.09%, respectively, and the average directional similarity reaches 91.10%, which is significantly better than other existing algorithms.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.05005

A Practical Post-Quantum Distributed Ledger Protocol for Financial Institutions

Yeoh Wei Zhu, Naresh Goud Boddu, Yao Ma, Shaltiel Eloul, Giulio Golinelli, Yash Satsangi, Rob Otter, Kaushik Chakraborty

原文摘要

Traditional financial institutions face inefficiencies that can be addressed by distributed ledger technology. However, a primary barrier to adoption is the privacy concerns surrounding publicly available transaction data. Existing private protocols for distributed ledger that focus on the Ring-CT model are not suitable for adoption for financial institutions. We propose a post-quantum, lattice-based transaction scheme for encrypted ledgers which better aligns with institutions' requirements for confidentiality and audit-ability. The construction leverages various zero-knowledge proof techniques, and introduces a new method for equating two commitment messages, without the capability to open one of the commitment during the re-commitment. Subsequently, we build a publicly verifiable transaction scheme that is efficient for single or multi-assets, by introducing a new compact range-proof. We then provide a security analysis of it. The techniques used and the proofs constructed could be of independent interest.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.28031

Models Recall What They Violate: Constraint Adherence in Multi-Turn LLM Ideation

Garvin Kruthof

原文摘要

When researchers refine ideas with large language models over several turns, do the models preserve the original brief's constraints? We introduce DriftBench, a benchmark for constraint adherence in multi-turn LLM-assisted scientific ideation. It pairs 38 research briefs from 24 scientific domains, each with hard constraints and banned moves, with four interaction conditions and seven models from five providers (two open-weight), scored on 2,116 runs. Iterative pressure to improve a proposal raises structural complexity in every model and lowers adherence to the brief's hard constraints. A restatement probe shows that the constraints stay legible and reproducible on demand while the proposal departs from them. The knows-but-violates (KBV) rate, the share of pressure runs scored non-compliant on a hard constraint despite a correct restatement, exceeds 50% for most models. A crossed-judge design measures the judge offset. GPT-5.4 scores adherence 0.535 points lower than Opus 4.6 on the same runs. The design finds no judge-by-condition interaction on adherence, so the pressure effect on adherence is judge-stable while KBV levels and the bottom of the model ranking are not. Re-eliciting the brief at checkpoints lowers KBV for some models but not others, and complexity inflation persists. Blind human raters find more constraint violations than the LLM judge, so reported adherence is conservative. We release all briefs, prompts, rubrics, transcripts, and scores.

cs.CL · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.06898

Self-Programmed Execution for Language-Model Agents

Luke J. O'Connor

原文摘要

At the heart of a language model agent is an orchestrator program which is responsible for the state transition between consecutive turns. In existing systems, this program is implemented in the harness. This paper introduces self-programmed execution (SPE), in which the model completion is itself the orchestrator program, and the harness evaluates this program but does not impose any fixed orchestration policy. Formally, an SPE state of an agentic machine is one from which a model completion can load any successor state up to an embedding, and an instance of an SPE state is one which executes the model completion as a program. In practice, the program requires a language with unusual features, as its source code doubles as the content of the model's context window. I therefore introduce Spell, a Lisp dialect in which programs can edit and re-evaluate themselves, and effectful expressions like model invocations are structured such that re-evaluating an edited program does not replay its side effects. Experiments with existing models, not trained for SPE or Spell, show that frontier models can learn Spell in-context and accomplish challenging agentic tasks. These results demonstrate how an LM can act as an agent without any fixed orchestration policy, and they raise the question of what self-orchestration strategies might be learned by a model trained for self-programmed execution. Code is available at https://github.com/lukejoconnor/spell.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.12151

Distinct mechanisms underlying in-context learning in transformers

Cole Gibson, Wenping Cui, Gautam Reddy

原文摘要

Modern distributed networks, notably transformers, acquire a remarkable ability (termed `in-context learning') to adapt their computation to input statistics, such that a fixed network can be applied to data from a broad range of systems. Here, we provide a complete mechanistic characterization of this behavior in transformers trained on a finite set $S$ of discrete Markov chains. The transformer displays four algorithmic phases, characterized by whether the network memorizes and generalizes, and whether it uses 1-point or 2-point statistics. We show that the four phases are implemented by multi-layer subcircuits that exemplify two qualitatively distinct mechanisms for implementing context-adaptive computations. Minimal models isolate the key features of both motifs. Memorization and generalization phases are delineated by two boundaries that depend on data diversity, $K = |S|$. The first ($K_1^\ast$) is set by a kinetic competition between subcircuits and the second ($K_2^\ast$) is set by a representational bottleneck. A symmetry-constrained theory of a transformer's training dynamics explains the sharp transition from 1-point to 2-point generalization and identifies key features of the loss landscape that allow the network to generalize. Put together, we show that transformers develop distinct subcircuits to implement in-context learning and identify conditions that favor certain mechanisms over others.

cs.LG · cond-mat.dis-nn · cond-mat.stat-mech

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09661

Video2Asset: Online Construction of Persistent 3D Street-Scene Assets

Zihan Zheng, Chunlei Cai, Zhenlong Wu, Linjun Li, Yichen Gong, Xiaoyun Zhang, Qiang Hu

原文摘要

Online 3D street-scene asset construction turns multi-view video into a continually updated scene of independently editable objects. The central challenge is to accumulate partial, intermittent observations under motion and occlusion without fragmenting identities or mixing evidence across objects. To address this challenge, we propose Video2Asset, an online framework for constructing persistent 3D assets from multi-view video. Using occupancy as an intermediate representation, we construct identity-aware observations with Historical Carrier Guidance for instance decoding and an identity encoder fusing instance-aligned 3D features with multi-view appearance. Accumulated-Evidence Association compares current observations with identity, geometric, and appearance evidence accumulated within asset records to determine identity inheritance and update world-coordinate states. We instantiate meshes from an asset library using these states and combine them with an occupancy-derived static background to support scene visualization, mesh replacement, object deletion, and trajectory editing. Experiments on several datasets demonstrate improvements in temporal identity consistency and asset quality across multiple occupancy models, including a 15.4% reduction in asset fragmentation on nuScenes with SGE.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09658

Differentially Private GraphRAG: Protecting Relation Connectivity and Semantics

Jie Fu, Yuan Hong, Yue Ning, Wendy Hui Wang

原文摘要

Graph Retrieval-Augmented Generation (GraphRAG) connects information across documents through entity relationships to support relational reasoning. However, black-box access to GraphRAG systems can enable adversaries to reconstruct knowledge graphs and recover both structural connectivity between entities and the semantics of their relationships. Existing defenses lack formal privacy guarantees and provide limited protection against such reconstruction attacks. We propose RelGuard, the first differentially private framework for GraphRAG, which provides formal privacy guarantees by mitigating connectivity leakage and protecting relations and their semantic meaning through relation removal. We formalize relation differential privacy (relation-DP), which bounds the distinguishability of mechanism outputs according to the semantic distance between alternative relations while accounting for relation removal. Building on this definition, we develop a four-step defense mechanism that sanitizes source documents before GraphRAG indexing by perturbing relation predicates or removing their associated connectivity using the exponential mechanism. Experiments across three GraphRAG systems and two targeted reconstruction attacks demonstrate that RelGuard provides stronger protection against reconstruction attacks than four existing defense mechanisms while better preserving downstream utility.

cs.CR · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03958

A Novel Conditional Adversarial Diffusion Model for High-Fidelity OCT-to-OCT Image Translation and Precise Anti-VEGF Therapy Response Prediction in Diabetic Macular Edema

Reza Rasti, Armin Biglari, Sobhan Mohammadi Hafshejani, Alireza Dehghani, Michael J. Allingham, Sina Farsiu

原文摘要

Diabetic macular edema (DME) is a leading cause of severe vision loss among working-age adults. Although intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections currently serve as the first-line therapy for DME, a notable subset of patients fails to respond despite the substantial cost and clinical burden, suggesting they may benefit more from alternative interventions. Automated, noninvasive pre-treatment response assessment could therefore spare non-responders from the current trial-and-error treatment paradigm. In this study, we propose a novel conditional adversarial diffusion framework that synthesizes post-treatment optical coherence tomography (OCT) images from baseline scans, enabling the derivation of each patient's initial response to anti-VEGF therapy. The model was developed with subject-level 5-fold cross-validation on a US cohort (n = 127) and externally validated on an independent Iranian cohort (n = 44). Benchmarked against fourteen baseline and state-of-the-art translation models, the proposed model achieved the best treatment-response prediction performance among the methods evaluated for that task on both cohorts, with balanced accuracies of 85.7% and 93.0% on the US cohort and 85.2% and 95.3% on the external Iranian cohort at the 10% and 20% central subfield thickness reduction thresholds, respectively. Ultimately, this framework offers a decision-support tool to help ophthalmologists anticipate initial anti-VEGF outcomes in DME prior to treatment initiation, toward personalized therapy selection. The source code will be made publicly available upon acceptance.

eess.IV · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09655

Decoupled Mode Connectivity for Base-to-Novel Generalization in Vision-Language Models

Imad Eddine Marouf, Khalid Oublal, Enzo Tartaglione, Stéphane Lathuilière

原文摘要

Prompt learning adapts vision-language models such as CLIP by optimizing a short learnable prompt. The cross-entropy objective pulls this prompt toward base-class specialization, whereas generalization to unseen classes benefits from staying close to CLIP's zero-shot feature space. Because both objectives act on the same prompt, their gradients conflict at convergence, and in our experiments no single-prompt loss design clearly improves on the cosine baseline. We propose Decoupled Mode Connectivity (DMC), which gives each objective its own prompt. Starting from a base-trained prompt, DMC trains a specialization prompt and a generalization prompt, and merges their text features with a fixed coefficient at test time, at the inference cost of a single prompt. A linear mode connectivity (LMC) corridor in text-feature space keeps the classification loss low for every classifier interpolated between the two endpoints, and a Visual Anchor KL term preserves CLIP's zero-shot class-similarity distribution of each image during specialization. We further argue that such regularizers should satisfy class-permutation invariance (CPI), and show via Fano's inequality, in an idealized model, that violating CPI lower-bounds the drop in novel accuracy by a term proportional to the mutual information between the prompt and the base-class labels. Across 11 datasets, DMC improves the average harmonic mean of base and novel accuracy over MergeTune for both CoOp and KgCoOp, and its interpolation curves pass beyond the single-prompt operating point, shifting the base-novel frontier rather than trading along it. Code is available at https://github.com/IemProg/DMC.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09652

Breaking the Privacy Promise: Gradient Inversion Attacks on Federated Low-Rank Adapted Vision Transformers

Ritik K. Badiya, Siladittya Manna, Ashmit Sinha, M Yashwanth, Anirban Chakraborty

原文摘要

Parameter-efficient fine-tuning (PEFT) has been increasingly adopted to fine-tune pretrained models with low computational cost and is therefore also preferred in the federated learning paradigm to reduce computational and communication overhead. In this work, we investigate the sparsely explored problem of the risk of private data leakage in LoRA-adapted Vision Transformers (ViTs), where low-rank updates weaken the gradient signal available to a server with access to individual client updates. We show that quality of reconstruction is limited by two coupled ambiguities: reduced information in LoRA gradients and missing spatial cues from frozen positional embeddings. To address this, we propose a spatially anchored generative inversion framework that incorporates server-prescribed trainable positional embeddings into ViT blocks during fine-tuning at clients. The resulting positional gradients act as layer-wise spatial anchors, providing additional positional cues, while the generative prior keeps reconstructions on a realistic image manifold, reducing duplicate-data and patch-permutation artifacts, {which produce similar gradients as those produced when the target client images are used}. Adding our proposed spatial anchoring to GIAS improves PSNR by 10.14 dB, SSIM by 0.45, and LPIPS by 0.098 on CIFAR-10, as well as PSNR gains of 7.83 and 11.74 dB on high-resolution CelebA and CARS datasets, respectively, in the FedSGD setting. Such gains in reconstructed image quality metrics are also maintained at higher batch sizes and in FedAvg settings. These results indicate that LoRA-based PEFT does not, by itself, prevent data leakage in FL, and carefully incorporated positional embeddings can help in exposing private information.

cs.CV · cs.AI · cs.CR · cs.DC · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2607.24532

From Machine Learning to Large-Scale EO Products: Good Practices and Guidelines for Making Maps

Ghjulia Sialelli, Robin Young, Cesar Aybar, Yuchang Jiang, Linus Scheibenreif, Damien Robert, Clemens Mosig, Kristof Van Tricht 等 15 位作者

原文摘要

The past decade has seen an explosion of large-scale geospatial products derived from Earth observation (EO) data using machine learning (ML). Driven by expanding model capacity and compute availability, the domain has reached a level of operational maturity where practitioners can generate high-resolution global products spanning thematic areas as diverse as human settlement, vegetation structure, and natural hazards. Deep learning has expanded the paradigm beyond locally calibrated models toward singular, globally applicable models trained on heterogeneous data, attracting a broad and growing community of practitioners from both the EO and ML sides. Yet, the resulting workflow introduces challenges that fall between established EO practice and mainstream ML: every stage presents tightly coupled technical challenges that recur across applications, and much of the practical knowledge needed to navigate them remains siloed within individual application domains. This guide brings together map-makers across the EO and ML communities, distilling their common practices, recurring pitfalls, and recommended approaches into a single end-to-end account, from raw satellite data to a published map product. Crucially, it treats the pipeline as an integrated workflow rather than a sequence of independent steps. We focus on wall-to-wall raster maps at moderate resolution (10-30m) and continental to global extent, produced with ML from open satellite archives, mainly Sentinel-1, Sentinel-2 and Landsat. The article is written for a deliberately broad audience, from those just entering the EO domain to ML experts building ML-ready EO datasets or generating large-scale maps.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09651

Concurrent Use of FP32 and FP64 Computing Units on GPUs and Mixed-Precision Placement in WENO Reconstruction

Jun Hatayama, Daichi Mukunoki, Yusuke Onishi, Atsushi Sekimoto, Toshinori Kouchi

原文摘要

Scientific computing often requires FP64 arithmetic, although FP64 performance is limited on NVIDIA GPUs. Double-float (DF) emulation, which represents a value using two FP32 values with about 48 bits of significand, outperforms native FP64 where FP64 units are limited. On the other hand, mixed-precision strategies offload to FP32 the operations that do not determine accuracy. In both approaches, the native FP64 units remain idle during FP32 instruction execution. We investigated whether this idle resource can be recovered on A100, GH200, GB200, and RTX PRO 6000 Blackwell Max-Q. A microbenchmark shows that co-resident FP32 and native-FP64 kernels exceed the throughput of the faster kernel alone on A100 but not on GH200 and GB200; on RTX 6000 Blackwell, they do so against FFMA but, against DF emulation, only when the issue density of the DF kernel is reduced. We then tested whether this gain carries over to computing the WENO-Z reconstruction with the nonlinear weights in FP32 and the candidate polynomials in native FP64 (W32P64). We observe substantial overlap on A100, whereas GH200, GB200, and RTX 6000 Blackwell show little overlap. Importantly, the fastest placement does not always preserve the design accuracy of WENO-Z: for a one-dimensional entropy wave problem, W32P64 fails to achieve the design order of accuracy for WENO7 and WENO9. In contrast, DF emulation of the entire reconstruction comes close to it on the grids tested for WENO5, WENO7, and WENO9 while delivering a $2.51-3.94 \times$ speedup over FP64 on RTX 6000 Blackwell. For WENO5 on the two-dimensional Euler vortex problem the W32P64 error at $n=256$ is $1.26 \times$ that of FP64 and 1/104 of that of the all-FP32 reconstruction.

cs.DC

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arXiv ↗PDF ↗
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 ↗