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新论文 · v1arXiv:2611.05632

Assurance-Conditioned Continuous Authorization for Autonomous AI Agents

Mehdi Shajari

原文摘要

Autonomous software agents can act through tools, delegate work, and change their models or execution environments while earlier authorizations remain in flight. We present Assurance-Conditioned Continuous Authorization (ACCA), an approach designed to be harness-independent, for maintaining operational authority according to currently justified assurance. It distinguishes evidence validity from contextual applicability, derives assurance claims from eligible evidence, and resolves a multidimensional effective-authority envelope within a principal-declared ceiling and applicable policy. A typed dependency graph supports selective re-evaluation of authorization-relevant changes. An authority epoch changes when effective authority changes; a distinct optional authorization-basis version supports revalidation of action-specific authorization conditions. Protected effect-time enforcement can reject stale actions before a consequential resource change, subject to the available commit mechanism. A compositional Domain Governance Profile expresses domain-specific semantics without granting authority. Controlled scenario tests implemented as a regression suite using a trading profile exercise default-deny rules, evidence-conditioned contraction and recovery, policy divergence, selective non-response, and stale-action rejection at a local gateway before the simulated-broker boundary. The study establishes implementation-level evidence in one domain, not a cross-domain guarantee of harmful-behavior prevention.

cs.AI · cs.CR

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.34474

Unconditional Lower Bounds for the Fredman--Khachiyan Algorithms for Monotone Boolean Duality Testing

Khaled Elbassioni

原文摘要

We prove unconditional lower bounds on the number of recursive calls made by the two algorithms of Fredman and Khachiyan (1996) for monotone Boolean duality testing, as a function of the explicit input size $N$. For Algorithm A we show a lower bound of $N^{Ω(\log N)}$ on the dual pairs $(\HH_k,\LL_k)$ obtained from the read-once family of Gurvich and Khachiyan. The proof lower-bounds the number of leaves of every binary decision tree computing the function by the modulus of its multilinear polynomial evaluated at a complex point of the unit circle. It therefore applies to every choice of branching variables and, more generally, to the class of two-child variable-restriction procedures whose terminal calls have polynomially bounded decision-tree complexity. On this family the number of calls is exactly $N^{Θ(\log N)}$. For Algorithm B, a symmetric specialization of the polynomial padding construction of Kobayashi, Kurita, and Wasa, with full polynomial blocks, yields at least $q^{(t-1)k-t(t-1)/2}$ calls, where $k=4t^2$ and $q=Θ(t^2)$; as a function of $N$ this is $N^{Ω(\sqrt{\log N/\log\log N})}$ along an infinite family. The same padded family forces Berge multiplication, for every edge ordering, to generate an intermediate family of size $N^{Ω(\sqrt{\log N/\log\log N})}$. In an appendix we show that applying the padding construction directly to Algorithm A gives $Ω(2^n)$ calls on a dual pair obtained from an unsatisfiable $n$-variable formula, which is only $N^{Ω(\sqrt{\log N/\log\log N})}$ in terms of the input size. No complexity-theoretic assumption is used. The proofs allow every branching-variable choice permitted by the respective algorithm. They concern the original recursive procedures, counting every invocation separately, without sharing calls or adding positive stopping tests.

cs.CC · cs.DS

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2508.09275

Constrained Black-Box Attacks Against Cooperative Multi-Agent Reinforcement Learning

Amine Andam, Jamal Bentahar, Mustapha Hedabou

原文摘要

Collaborative multi-agent reinforcement learning (MARL) is increasingly deployed in critical domains, yet its vulnerability to adversarial perturbations has not been thoroughly investigated. Most existing adversarial attacks against MARL rely on optimistic assumptions, such as access to policy weights or the ability to train surrogate policies. We investigate whether adversaries with limited capabilities remain effective against MARL agents. In particular, we study two restricted attack settings that are overlooked in the literature: (i) adversaries with access only to agent observations and (ii) adversaries with no access whatsoever. Our approach generates perturbations that intentionally misalign how victim agents perceive their environment, preventing them from collaborating. We conduct extensive experiments across a diverse set of 22 environments. Our results demonstrate both the effectiveness and sample efficiency of our attacks.

cs.LG · cs.MA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05626

Exact signal recovery from generative priors with sigmoid output activation

Matthew S. Scott, Yaniv Plan, Özgür Yılmaz

原文摘要

We show exact signal recovery in generative compressed sensing for feedforward neural networks with piecewise-linear activations and a final sigmoid activation, with no assumption of expansivity. Consider a ReLU network of depth $d$ with a sigmoid final activation, whose input layer, of dimension $k$, is the narrowest, and let $K$ be the geometric mean of the widths of its layers. We show that, with high probability, all signals of the range whose entries lie in $[δ, 1 - δ]$ are uniformly recovered from $m \gtrsim k(d \log(K/k) + \log(1+\log(1/δ)))$ measurements.

cs.IT · eess.SP · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05625

Personalized Visual Support After Automatic Speech Recognition

Beatriz Calero, Duarte Neves, Carlos Bruno, Luis Ricardo Santos, Mario Egea

原文摘要

Many spoken-language interfaces stop at producing a transcript. This project examines how a second layer of optional text and pictorial support can be adapted to individual needs. We specify four presentation levels and a behavior-based selection policy driven by replay requests, response latency and recent comprehension. After expert review of the mappings, we will compare fixed and adaptive presentation using counterbalanced spoken instructions. The study is designed to measure comprehension, usability and which modality listeners rely on, not to claim that transcript embellishment uniformly benefits listeners.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.11319

Neural networks become centroid classifiers when trained on heavily corrupted inputs

Justin Tahmassebpur, Asadullah Bhuiyan, Hyejin Kim, Omri Lesser

原文摘要

Learning from imperfect data is a central theme in machine learning, connecting practical questions of robustness to fundamental questions of learnability. Here we examine attribute noise: learning from corrupted inputs while keeping the labels intact, a setting that has received considerably less analytical attention than its label-noise counterpart. We consider two types of corruption models: additive noise and replacement noise. Through experiments with multi-layer perceptrons (MLPs) on corrupted classification datasets, we find that neural networks remain robust, maintaining well-above-chance accuracy even when inputs are >90% corrupted -- far beyond human recognition. To understand this robustness, we analyze infinite-width networks in the heavy-corruption regime using a mean-field-inspired approach and derive a leading-order decision rule for the classification outcome: the network implements a prototype rule, the nearest-class-mean, assigning each test point to the class whose training-set average it most closely resembles. This leading-order decision rule is universal across a broad range of MLP architectures, holding for any depth, as well as a wide class of activation functions and noise distributions. The same centroid mechanism closely matches finite-width network behavior in our experiments and provides an interpretable and analytically tractable account of why learning can succeed even when individual training examples carry almost no signal.

cs.LG · cond-mat.dis-nn

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.21257

Fractional Power-of-Two Quantization for Efficient and Effective Multiplier-Free LLM Inference

Sunghyun Wee, Geunjae Choi, Hyeonjin Kim, Suyoung Kim, Nojun Kwak

原文摘要

Large Language Models (LLMs) demand efficient inference, where the multiply-accumulate (MAC) operations in linear projections dominate compute and energy cost. Post-training quantization (PTQ) reduces this cost by mapping weights and activations to low-bit integers. Power-of-Two (PoT) weight quantization promises efficient multiplier-free LLM inference via shift-add logic, but suffers significant accuracy degradation at low bit-widths due to its coarse base-2 quantization grid. We introduce Fractional PoT (FPoT), a base-$\sqrt{2}$ (half-octave) fractional PoT grid that achieves practical 4-bit weight quantization while preserving a fully integer shift-add datapath. The method combines (i) the base-$\sqrt{2}$ weight grid with uniform integer activations, (ii) a calibration-time grid-alignment refinement absorbed into the static per-channel scale, and (iii) a lightweight 1-adder $\sqrt{2}$ approximation whose error is absorbed during weight calibration via approximate-grid quantization, yielding a processing element (PE) with significantly fewer gates than a 4-bit integer multiplier. On Llama-3-8B with 4-bit weight and activation quantization, FPoT improves zero-shot commonsense reasoning performance by 8.72% relative to the PoT baseline, while maintaining performance close to the uniform integer baseline, with only a negligible 0.4%p degradation. These results hold across various configurations spanning seven models and three bit-widths. Furthermore, FPoT can be integrated with state-of-the-art LLM PTQ methods, demonstrating framework-agnostic applicability.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06954

Certified folding-free deformable image registration: A whole-cell Jacobian certificate and a sequential variational solver

Baihan Zhao, Zicong Zhou, Guojun Liao

原文摘要

Registration maps propagate labels, measure local volume change, and map results back through their inverses; requiring invertibility. Voxelwise checks can miss folds between grid points. We study the canonical continuous extension of a nodal displacement field by trilinear interpolation and give a test that proves its Jacobian determinant positive in every grid cell or rejects the map. The test is rigorous under IEEE 754 binary64 arithmetic (correct rounding, no contraction). With a fixed boundary, certification implies a bi-Lipschitz homeomorphism of the image domain. We introduce SeqVP, built around this certificate. A proximal Jacobian-determinant-curl formulation yields inner problems whose penalties on changes in the map, Jacobian determinant, and curl vanish to first order at the current iterate. The method decreases a fixed outer energy up to a floating-point resolution guard and certifies every committed iterate. After certified initialization, stopping at any time returns a folding-free map. A Poisson-preconditioned gradient method with Jacobian-aware step prediction and affine-scaling damping, initialized by certified handoff from an uncertified binary32 pre-optimizer, handles a 1 mm brain MRI with about 2e7 unknowns in 90 s on one GPU. On 49 held-out OASIS pairs, SeqVP achieves mean Dice 0.821 over 35 structures and 0.889 over 4 tissue classes, with certified zero folding. Gains are 0.022 and 0.025 over ANTs SyN, and 0.022 and 0.023 over uniGradICON with instance optimization at equal time (paired, Holm-adjusted p < 0.001), but maps are rougher. SeqVP is level with ANTs on LPBA40, behind it on IBSR18, and ahead of all label-free baselines on 20 large-deformation pairs. On fields from nine methods, the certificate finds non-positive-Jacobian cells in every ANTs SyN map (median 10, missed by voxelwise checks) and in maps from NiftyReg, ConvexAdam, uniGradICON, and TransMorph.

cs.GR · math.AP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.09502

Recovery Theory for Projected Power Iterations in Permutation Synchronization

Vahan Huroyan, Gilad Lerman

原文摘要

We study the projected power method for permutation synchronization: recovering $n$ unknown permutations of $m$ objects from pairwise relative permutations, observed independently with probability $p$ and independently corrupted by uniform permutations with probability $1-π_0$. Our main theorem establishes uniform one-step contraction for all estimates with optimally aligned block-error fraction at most $1/2-ε$, for fixed $ε>0$, including measurement-dependent estimates. When $np\ge C_0\log(n)$ and $m=o(npπ_0^2)$, one update contracts the error by a factor $O(m/(npπ_0^2))$, up to a floor of $O(e^{-c\min\{m\,npπ_0^2,\,npπ_0\}} + \log(n) / n)$. Thus, every initialization in this majority basin achieves $o(1)$ error after one update and maintains it uniformly over subsequent iterations. A reference-block spectral initializer enters the basin with probability $1-o(1)$, yielding end-to-end almost-exact recovery. If additionally $\min\{m\,npπ_0^2,\,npπ_0\} \ge C\log(n)$ for sufficiently large $C$, the method achieves exact recovery in finitely many iterations. We also establish prescribed-block recovery under weaker conditions for measurement-independent estimates and extend the guarantees to independent permutation-valued corruptions with uniform mean and to partial permutations with common visible support.

stat.ML · cs.IT · math.OC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2507.10880

Domain-Adaptive Small Language Models for Structured Tax Code Prediction

Souvik Nath, Sumit Wadhwa, Luis Perez

原文摘要

Every day, multinational firms process thousands of transactions, each of which must adhere to tax regulations that vary by jurisdiction and are often nuanced. The determination of product and service tax codes, such as HSN or SAC is a major use case in Tax compliance. An accurate determination of such codes is imperative to avoid any tax penalties. This paper proposes a domain-adaptive small language model (SLM) with an encoder-decoder architecture for the enhanced prediction of product and service tax codes. In this approach, we address the problem of predicting hierarchical tax code sequences using unstructured product and services data. We employ an SLM based upon encoder-decoder architecture as this enables sequential generation of tax codes to capture the hierarchical dependencies present within the tax codes. Our experiments demonstrate that encoder-decoder SLMs can be successfully applied to the sequential prediction of structured tax codes, a domain that remains comparatively unexplored in current NLP research. In this paper, we demonstrate the superior performance of the domain-adaptive encoder-decoder SLMs over flat classifiers when applied to the Harmonized System of Nomenclature (HSN), and achieve superior results compared to decoder-only and encoder-only architectures for structured sequence generation tasks. This approach can also be scaled to other government-mandated tax commodity codes, such as United Nations Standard Products and Services Codes (UNSPSC), or Brazil's Nomenclatura Comum do Mercosul (NCM).

cs.LG · cs.CL

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.03792

What Do Verifiable Rewards Teach Video-Language Models About Time? A Controlled Multi-Model Study

Avyay Sadhu, Patrick Cooper

原文摘要

Reinforcement learning from verifiable rewards (RLVR) has produced large reasoning gains in language models, and verifiable video benchmarks make the recipe directly applicable to causal-temporal video question answering. We present a controlled study of what RLVR actually teaches video-language models about temporal structure. We fine-tune four open models with group relative policy optimization (GRPO) under three data recipes using annotated synthetic questions, self-supervised real-video pretext tasks, and a one-to-one mixture, plus a targeted real-video augmentation arm. We evaluate the model-recipe grid on 105 CLEVRER questions with one correct option and on two real-video transfer sets. The 8B ordered gain is 4.8 points (95% video-bootstrap CI [-5.6, 15.1]), while blind accuracy increases 10.5 points ([0.9, 20.6]). The 4B model gains an accuracy advantage for ordered over shuffled frames. On the real-video sets, the 8B synthetic-only checkpoint loses 26.7 and 25.2 points, with smaller regressions under mixed and augmented recipes. In a separate scene-disjoint, single-gold Qwen3-VL-8B experiment, supervised fine-tuning (SFT) and GRPO each complete 1,200 updates at three fixed seeds. GRPO minus SFT is -3.89 points in ordered accuracy (nominal 97.5% video-bootstrap CI [-7.36, -0.45]) and -2.78 points in ordered-minus-shuffled accuracy ([-6.60, 0.87]). Transfer differences are sensitive to parseability. Intervals quantify test-video uncertainty conditional on the fitted runs. The results show why benchmark accuracy, transfer, and presented-order sensitivity need to be evaluated separately.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2512.20773

DIAL: Direct Iterative Adversarial Learning for Realistic Multi-Turn Dialogue Simulation

Ziyi Zhu, Olivier Tieleman, Caitlin A. Stamatis, Luka Smyth, Thomas D. Hull, Daniel R. Cahn, Jinghong Chen, Matteo Malgaroli

原文摘要

Realistic user simulation is crucial for training and evaluating multi-turn dialogue systems, yet creating simulators that accurately replicate human behavior remains a significant challenge. An effective simulator must expose the failure modes of the systems under evaluation. This work introduces Direct Iterative Adversarial Learning (DIAL), an adversarial framework that iteratively enhances user simulator realism through a competitive dynamic between a generator (user simulator) and a discriminator. When applied to mental health support, a domain characterized by diverse failure types and a critical dependence on realistic user behavior for failure detection, DIAL restores lexical diversity diminished by supervised fine-tuning and drastically reduces discriminator accuracy. The resulting simulator exhibits a strong correlation between simulated and real failure occurrence rates while maintaining low distributional divergence of failure modes. These findings indicate that DIAL is a promising method for developing realistic user simulators in multi-turn dialogue, facilitating reliable and cost-effective system evaluation prior to deployment.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.36418

When the Application Layer Is Disposable: Putting Integrity Constraints Back in the Database

Hasara Kalumin, Amol Deshpande

原文摘要

Integrity constraints have been central to the relational model since its inception. However, beyond keys, single-row CHECKs, and referential integrity, most constraints are enforced by application layers today. SQL ASSERTIONs, standardized in SQL-92, remain poorly supported by major RDBMSs. This has become increasingly unacceptable due to two developments. First, LLM-driven applications and agents increasingly issue queries and updates directly against databases, bypassing application layers where invariants reside. Such code may be generated on demand and discarded afterwards, making the database the only durable component that can encode and enforce these invariants. Second, natural constraints in higher-level conceptual models, such as E/R and property graphs, become fragmented across relations under mappings chosen for performance. The resulting multi-relation constraints lack direct database support, and only some can be efficiently enforced using existing mechanisms such as triggers. These developments motivate an overhaul of how constraints are defined, mined, managed, and enforced in data management systems. As a first concrete step, we introduce constraint indexes, which represent constraints spanning multiple relations as transactional access paths, and implement a proof of concept as a PostgreSQL extension. Constraint indexes enforce constraints arising from distributed inheritance hierarchies, folded relationships, and folded weak entities, as well as cardinality bounds and functional dependencies, at low overhead relative to no enforcement. Across these workloads, they outperform the better of the correctness-preserving baselines by up to 2.41 times. We also show that LLM-driven writers frequently violate unenforced constraints and behave erratically when provided with a list of constraints to enforce. We close by discussing a few research challenges raised by this agenda.

cs.DB

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05622

Almost All 3-uniform Hypergraphs are Class 1

Arnab Chaterjee, Asaf Ferber, Marcelo Sales, Mason Shurman

原文摘要

The chromatic index $χ'(H)$ of a hypergraph $H$ is the smallest number of colors needed to color its edges so that no two adjacent edges share the same color. The chromatic index is trivially bounded below by the maximum degree $Δ(H)$, and when $χ'(H)= Δ(H)$, we say that $H$ is Class 1. It is well known that almost every 2-graph is Class 1, and we extend this result to 3-graphs. We show for $n^{-2/3-1/200}\leq p \leq 0.99$ that the $3$-uniform binomial random hypergraph $H\sim H_{n,p}^{(3)}$ is Class 1 with high probability. We also show that this result doesn't hold for uniformities above $3$. For a broad range of $p$, when $k \geq 4$ and $k \nmid n$, the hypergraph $H \sim H_{n,p}^{(k)}$ is with high probability not Class 1.

math.CO · cs.DM

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.38970

Communication-Efficient Clustered Federated Learning in Non-IID Wireless Networks

Martin Isaksson, Rickard Cöster, Šarūnas Girdzijauskas, Seif Haridi

原文摘要

Current and emerging mobile networks face increasing privacy, scalability, and performance challenges as distributed learning becomes essential for advanced 6G services. These challenges are amplified by the presence of non-IID data, naturally arising in heterogeneous wireless environments, that degrade the performance of standard Federated Learning (FL) approaches. To address non-IID data across clients, we propose a Clustered Federated Learning (CFL) framework that assigns clients to specialized models during training. Data-domain clustering initializes model-based clustering, enabling identification and exploitation of client similarities without sharing raw data while reducing communication cost. Motivated by mmWave beam selection, we evaluate our methods on a wireless simulation dataset (DeepMIMO) and complement it with two non-IID benchmarks (FEMNIST, Rotated CIFAR-10) to isolate different non-IID characteristics. Our best configurations improve accuracy over IFCA by 4.8 pp on DeepMIMO and 10.6 pp on FEMNIST while reducing downlink data volume by 83.1 % on FEMNIST compared to IFCA. We demonstrate that different clustering strategies suit different non-IID characteristics. These results show that our CFL methods enable resource-efficient, and high-performance distributed learning in future 6G networks.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2607.17082

OTAP: Structure-Aware Optimal Transport for Evaluating Planning and Execution in Agent Trajectories

Babak Barazandeh, Subhabrata Majumdar, George Michailidis

原文摘要

Large language model agents solve tasks by generating trajectories that interleave planning, tool calls, and intermediate results. Current evaluation metrics reduce such a trajectory to a binary success flag, compare it against a reference by exact matching, or delegate judgment to another language model. A success flag cannot distinguish sound solutions from lucky ones, nor explain why runs fail. Exact matching penalizes valid plans reordered or decomposed differently. We reframe trajectory evaluation as a distance between the agent's execution graph and a set of valid solution graphs, and instantiate it via an unbalanced fused Gromov-Wasserstein transport problem over attributed dependency graphs. The resulting score, termed OTAP (Optimal Transport for Agentic Planning), is a normalized discrepancy score. Its idealized, globally minimized entropy-free objective is invariant to dependency-preserving relabelings of a fixed graph and has a redundant-insertion sensitivity analysis under stated assumptions. Its unbalanced marginals handle missing or hallucinated steps without forcing a match; granularity invariance remains an open problem that soft coupling mitigates only in part. On controlled perturbations and three public benchmarks, OTAP separates valid from invalid trajectories in a regime where semantics-only metrics score below chance. Its performance is encouraging across these settings, while controlled edge-noise experiments reveal sensitivity to dependency extraction. Where a formal verifier exists, strict surface metrics predict validity better than OTAP does, which places OTAP in open-ended domains where no verifier is available. A blinded study of 236 trajectories finds the strongest agreement with OTAP among the six compared metrics. Structure-aware baselines achieve higher clean-graph AUROC, while OTAP has the smallest absolute AUROC decline under the tested edge noise.

cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05620

ViBench 2.0: A Benchmark for Long-Horizon Software Building

Daniel Furman, Preeya Kirani, Ibrahim Sheikh, Zhen Li, Peter Zhong, Michele Catasta

原文摘要

On Replit, our agentic engineering platform, users now build entire products without writing code, through successive feature requests to an agent. Yet existing coding benchmarks evaluate single, fully specified tasks, and many are near saturation. We introduce ViBench 2.0, a benchmark for long-horizon software building that we use to select our platform's models. Its 17 full-stack web products, modeled on widely used software, grow over 241 feature requests and are judged only when finished, as a user would judge them: a browser agent runs 544 test plans, and a build passes only if every plan does, under a grading protocol validated to keep this strict score reliable. Evaluating nine models, we find a reliability gap: the three strongest pass up to 98% of test plans yet deliver a fully working product in at most 65% of builds, and no open-weight model delivers one. The gap is closing fast: across three releases of one model family, pass@1 on the public products rises from 3% to 53% while the cost per build falls by more than half. We release eight products with the harness and grader, hold out nine, and report lessons from production use.

cs.AI · cs.SE

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05618

When can multi-fragment 2D/3D registration be trusted? Calibrated certification and uncertainty-driven view planning for pelvic fracture fluoroscopy

Bowen Xiang, Michael I. Miga

原文摘要

Registering a segmented CT to fluoroscopy can locate every fracture fragment but can guide surgery only when trusted. After each shot, our closed loop registers all fragments, bounds their errors with a calibrated sandwich covariance, flags implausible or ambiguous ones, and stops or plans the next view. On simulated fluoroscopy of 60 held-out fracture CTs with realistic image uncertainty, 95% bounds covered 83% of unflagged fragments and 91% of those within 5mm, but from the second view on 14% failed by over 5mm, a third with bounds of at most 2mm. A fragment's shift when a view was added separated tightly bounded failures best (exploratory; AUC 0.94, bound 0.81). Requiring that the newest view moved no fragment beyond its bound, preferred post hoc, lowered false stops at 2mm from 23% to 6% overall, with 345 instead of 362 correct ones but more trajectories uncertified; delaying the stop did not, and other signals at equal acceptance left 10-15% false. With a segmentation error shared by all views (median Dice 0.89), 21% of this rule's stops were false against 10% without it (15 cases; exploratory). Minimax planning certified sooner than the clinical protocol without the check (4.20 versus 4.72 shots, Holm p <0.001), but no criterion reached the pre-specified endpoint, a true 2mm error, significantly sooner, even in the true basin. On real fluoroscopy of five intact pelves no 2mm certificate was false. Trust takes evidence across views, not only a calibrated local bound.

eess.IV · cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.12438

Parallel Edge Ranking of Trees

Jeff Giliberti, MohammadTaghi Hajiaghayi, Changki Yun

原文摘要

In this work, we prove that computing the edge ranking of a tree in parallel is P-Complete, and complement this hardness with new algorithmic results for both PRAM and the MPC model. An optimal tree edge ranking assigns positive integer ranks to the edges such that any two edges with the same rank are separated by an edge of higher rank, while minimizing the highest rank. Although an optimal edge ranking, which minimizes the maximum rank, can be computed in polynomial time sequentially, its parallel complexity has remained open for almost 35 years [de la Torre, Greenlaw, Schäffer, SODA'93] and was listed as an open problem in Limits to Parallel Computation [Greenlaw, Hoover, Ruzzo, 1995]. Specifically, we show that deciding whether a tree has edge ranking at most $K$ is P-complete, already for trees of diameter at most six. Our reduction encodes monotone circuit evaluation into a greedy allocation procedure underlying known sequential algorithms. Using a rooted product of trees, we further prove that computing an edge ranking within constant additive error remains P-hard. On the algorithmic side, we break the long-standing 2-approximation barrier by designing a deterministic NC algorithm that computes an $(1+o(1))$-approximate edge ranking. Second, we show that P-completeness can be bypassed in Massively Parallel Computation (MPC) with strongly sublinear local memory: we give a deterministic algorithm that computes an optimal edge ranking of an $n$-vertex, diameter-$D$ tree in $O(\log D + \frac{n^{3/4}D^{1/4}}{S})$ rounds with $S = Θ(n^{3/4+δ})$ local memory, for $δ\in (0, 1/4)$, requiring only $O(1)$ rounds on the hard diameter-six instances. This result reinforces the separation between $\mathsf{NC}$ and what can be computed efficiently in MPC.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02197

Conditional Diffusion-Based Generation of T2-Weighted Prostate MRI for Clinically Significant Prostate Cancer Classification

Xavier Rafael-Palou, Alejandro Vergara-Richart, Ignacio Iborra Roncales, Carmen Prieto-de-la-Lastra, Andreu Antolin-Redondo, Juan Morote, Ana Jimenez-Pastor

原文摘要

Scarce and unrepresentative training data limit the performance of prostate MRI prediction models. Synthetic T2-weighted (T2W) MRI may augment training data containing clinically significant prostate cancer (csPCa) and transition zone (TZ) lesions. In this retrospective, single-center, ethics-committee-approved study, we developed a conditional generative model for T2W prostate MRI extending MAISI-v2 with csPCa class, zonal, and lesion-mask-guided conditioning, incorporating a relative advantage weighted loss fine-tuned on 1,303 patients (1,654 lesions). Fidelity was assessed by incremental ablation, and downstream utility was evaluated with T2W-based csPCa classifiers across five simulated training-data scenarios, each testing 18 synthetic-data configurations. The proposed model reduced Fréchet inception distance (FID) from 13.28 (pretrained MAISI-v2) to $2.04 \pm 0.06$ internally (PI-CAI: $3.83 \pm 0.51$). Synthetic augmentation significantly increased AUC in all five scenarios (+0.027 to +0.088; adjusted $p = 0.016$--$0.043$), though cross-institutional augmentation of PI-CAI yielded a non-significant gain (+0.013; $p = 0.397$). Overall, our model improved T2W MRI fidelity, and synthetic images improved csPCa classification under simulated data constraints, although benefit did not transfer significantly across institutions.

cs.CV

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新论文 · v1arXiv:2611.05616

LACS: Distribution-Sensitive Label-Agnostic Confidence Selection for Dynamic Data Pruning

Heejin Choi

原文摘要

Training on large datasets motivates selecting which examples receive expensive training computation. Fixed quotas determine the amount of pruning in advance, while absolute confidence cutoffs depend on the score scale. We study a population-relative alternative that adapts the threshold and retained count together. We introduce LACS (Label-Agnostic Confidence Selector), a training-time rule based on maximum predictive confidence, requiring neither labels at selection time nor per-example gradients. LACS removes examples at or above a log-mean-exp reference plus a margin. We characterize its sensitivity to score gaps and mass, and derive an exact no-pruning condition. For $0<ρ<1$, the removed fraction is at most $ρ$ and at least one example remains. The unsigned score permits both confidently correct and confidently wrong examples to be removed. Seven evaluation settings include neural training, language modeling, and boosting. Neural execution studies reduce backward-active rows by 10-35%; on CIFAR-10, 29% fewer backward rows accompany accuracy means of 89.77% versus 89.82% for full CE. At a matched backward budget and number of optimizer updates, LACS is 1.0 pp above small-batch training and 1.2 pp above random selection over five seeds. Across 14 tabular datasets, quadratic boosting reduces mean test error from 23.36% to 22.96%. Its full training replays use about 30% fewer fitting rows; these counters span a different horizon from validation-selected accuracy. In one RBF-SVM case study, elapsed time falls by 37% with slightly lower error. These findings separate selective-work reduction from realized acceleration: the current exact-current-score neural implementation uses an additional scoring forward, so its backward sparsity does not yet translate into wall-clock acceleration. This overhead is an execution choice rather than a requirement of the selection rule.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05615

$\mathcal{C}^\infty$ Smooth Trajectory Planning with Application to Soft Robots

Reinhard M. Grassmann, Abdulaziz Y. Alkayas, Lingyun Chen, Sami Haddadin

原文摘要

High motion performance and avoiding structural vibrations rely on smooth trajectory planning. The smoothness requirement depends on the robot's complexity in terms of the number of degrees of freedom and system order due to the flexibility of the joints and links. For industrial robots, the smoothness requirements have been determined and subsequently taken into account. Although unknown for soft continuum robots, the exact smoothness requirement can potentially be much larger than previously considered ones such as $\mathbf{\mathcal{C}^4}$ for collaborative robots and $\mathbf{\mathcal{C}^5}$ for quadcopter drones. To address this gap, we introduce a $\mathbf{\mathcal{C}^\infty}$-smooth trajectory generator that respects defined kinematic limits and generates infinitely differentiable trajectories. The generated path addresses and generalizes to all smoothness requirements, where a transition between sequential states is needed. Therefore, all trajectories have continuous velocity, acceleration, jerk, snap, crackle, pop, etc. Of particular interest is the case of null boundary conditions, i.e., all derivatives at the start and end of the trajectory are equal to zero. We verified and evaluated our proposed approach in simulations and on hardware.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05614

The CNF Encoding Complexity of Boolean Functions

Andrew Krapivin, Benjamin Przybocki, Bernardo Subercaseaux

原文摘要

We study the minimum number of clauses required to encode a boolean function into a CNF formula with auxiliary variables. Our main result is that if a boolean function can be evaluated in nondeterministic time $t$ (in the RAM model) with $m$ bits of advice such that at most $b$ bits of advice are accessed for each input, then it admits a CNF encoding with $\widetilde{O}(t + \sqrt{mb})$ clauses. This constitutes a nonuniform generalization of Robson's (TCS'91) variant of the Cook--Levin theorem, for which a naïve generalization would yield $\widetilde{O}(t+m)$ clauses; our applications leverage $b = o(m)$ to obtain significantly better bounds. Since preprocessed data structures can be modeled as computation with advice, our result allows us to translate space- and time-efficient data structures to compact CNF encodings. As an application, we derandomize a data structure of Larsen and Williams (SODA'17) to show that every $k$-CNF function on $n$ inputs can be encoded with $n^k/2^{Ω_k(\sqrt{\lg n})}$ clauses. Notably, despite encoding a $k$-CNF function, the encoding itself necessarily contains clauses of unbounded width, since an information-theoretic argument shows that the minimum number of bounded-width clauses to encode such functions is $Ω_k(n^{k}/\lg n)$ in the worst case. In general terms, our results imply that for a wide variety of NP-complete problems, one can reduce to CNF-SAT in a way that results in asymptotically fewer clauses than previously known methods.

cs.CC · cs.LO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05611

Hybrid Spark: Adaptive Speculative Decoding That Lets the Content Choose the Drafter

Ekagra Ranjan, Bharat Venkitesh

原文摘要

Speculative decoding (SD) speeds up large language model (LLM) serving, but its gain depends on the workload's \emph{shape}, such as batch size and context length, and on its \emph{content}. A model-based drafter proposes a short block on any content, while a model-free drafter such as n-gram lookup proposes long copies at almost no cost when the output repeats its input, as in code editing. Adaptive verification spends a batch-wide verification budget on the draft tokens most likely to be accepted, but it supports only one model-based drafter. Model-free drafters provide no per-token confidence, so they cannot take part in budget allocation, and deployments must either fix one drafter or split their GPUs between drafters. We present \sys, a framework that runs a model-based and a model-free drafter on every step and gives each request the better of their chains, within adaptive verification and entirely on the GPU, which keeps asynchronous scheduling intact. The key idea is a price per verified draft token: the goodput that slot would earn elsewhere in the batch. Once the model-free drafter's tokens carry calibrated acceptance estimates, fit once on general text, this price puts both drafters' chains on one scale. Instantiated with \dspark and an n-gram drafter in vLLM, \sys achieves 1.10--1.62\x the output throughput of \dspark on code editing at batch size~4 and 1.04--1.27\x at batch size~128, while staying at 0.97--1.01\x of it on chat at temperature~0. A single deployment can therefore serve both workloads.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.02170

Restrained Diffusion Bridges: Targeted Denoiser Training and Refinement

Xuanxi Zhang, Kevin S. Cheng, Weizhou Wang, Aaron R. Dinner, Jonathan Weare

原文摘要

Diffusion models are often inaccurate in critical, data-sparse regions of the target distribution. Sampling from restrained distributions, biased distributions used in classical enhanced sampling to concentrate data near chosen configurations, can control where training data are collected but biases the learned distribution. We establish that this bias cancels exactly when forward noising from restrained data is conditioned on the restrained configuration at an appropriately chosen intermediate time. This allows a pretrained model fine-tuned on restrained data to achieve high accuracy in any chosen region of the target distribution, regardless of its probability, using standard denoising or flow matching objectives without importance weights. When no target data are available, Gibbs sampling enables inference from a model trained exclusively on restrained data.

cs.LG · physics.chem-ph

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.13734

PolicyMem: Geometric Policy Memory for LLM Governance

Yuanchen Bei, Zhengzhang Chen, Yanjun Zhao, Haoyu Wang, Hanghang Tong, Haifeng Chen

原文摘要

Effective LLM governance extends beyond detecting unsafe responses to guiding their correction through closed-loop assessment, revision, and verification. Learning-based guards provide strong semantic discrimination but often leave policy knowledge implicit in trained parameters, limiting its explicit reuse across stages. Programmable guardrails make policies explicit through prompts and workflows, but rely heavily on manual engineering to operationalize them. This motivates explicit policy representations that are both learnable and reusable across governance operations. To this end, we introduce PolicyMem, a geometric policy memory that compiles natural-language policies into learnable low-rank subspaces for reuse across governance stages. A policy assessor projects query-response representations onto these subspaces, producing evidence that directly mediates safety judgments and policy attribution. A response rewriter uses the implicated policies to revise unsafe responses, with the same assessor re-evaluating the revised responses. Beyond inference, the same memory constructs training data from model-generated candidates to supervise the rewriter without external teacher calls. Across five widely used benchmarks, PolicyMem achieves the best average unsafe-behavior detection performance among representative baselines while supporting effective policy attribution, rewriting, and post-intervention assessment through the shared policy memory.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.05609

On the Generalization Limitation of On-Policy Distillation for Post-RL Consolidation

Yichen Wang, Yufa Zhou, Siqi Zhu, Haozheng Luo, Jingzhi Bao, Zhichao Liu, Zijun Wei

原文摘要

Multi-teacher on-policy distillation (MOPD) extends on-policy distillation (OPD) to consolidate capabilities from multiple teachers. Although prior work reports near-perfect in-distribution (IND) distillation performance, we find that this can mask incomplete capability consolidation beyond the supervised distribution. A preliminary study on synthetic reasoning shows that OPD does not fully transfer teachers' generalization capabilities. We then extend this investigation to real-world tasks, evaluating extrapolative and compositional generalization across mathematical reasoning, coding, agent, and long-context capabilities using Qwen3 models. Across extrapolative settings, OPD and MOPD reach 96.04-102.73% of their teachers' IND scores. Yet their out-of-distribution (OOD) scores remain 2.45-16.25% below the base model's. Across three compositional settings, MOPD improves IND performance over the base model, but its performance on composed tasks as OOD remains 18.19-24.34% below that of the best teachers. To understand these gaps, we examine teacher capability and data coverage as two potential bottlenecks. Jointly alleviating these bottlenecks eliminates OOD forgetting in our experiments and suggests the potential for generalization beyond the teacher. (M)OPD also reduces OOD forgetting on average relative to its reinforcement learning (RL) baselines. Analysis of the associated update geometry reveals smaller and more spectrally concentrated updates on average than those of the RL baselines, along with greater shared update structure under MOPD. Motivated by these findings, we propose geometry-based patching and base-preserving replay to improve OOD performance while largely preserving IND performance. Our findings identify a blind spot in capability consolidation via (M)OPD, highlighting the need for algorithms that explicitly support OOD retention and generalization.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.19544

Reliability without Validity: A Systematic, Large-Scale Evaluation of LLM-as-a-Judge Models Across Agreement, Consistency, and Bias

Justin D. Norman, Michael U. Rivera, D. Alex Hughes

原文摘要

LLM-as-a-Judge has become the dominant evaluation paradigm for language models, but judge validation in practice relies on exact-match agreement, a metric that does not correct for chance and systematically overstates discriminative ability. We present the largest systematic evaluation of LLM-as-a-Judge to date: 21 judges from nine providers across MT-Bench, JudgeBench, and RewardBench, evaluated under three protocols (agreement, consistency, bias audit) over 118 runs and approximately 541,000 individual judgments. Four findings emerge, consistent across the full cohort, including the April 2026 frontier: kappa deflation between exact match and Cohen's kappa is universal (33--41 pp on MT-Bench), judge rankings shift by up to 14 positions across benchmarks, high test--retest reliability (>0.95) coexists with severe position bias (>0.10) in two production-deployed judges (instantiating a consistency--bias paradox), and verbosity bias is small (<0.011) across our cohort under a single pairwise rubric. We distill these into a Minimum Viable Validation Protocol.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.05017

Hyperparameter Transfer in Graph Neural Networks

Gage DeZoort, Boris Hanin

原文摘要

The performance of deep learning models crucially depends on the settings of hyperparameters like learning rate, initialization scale, and weight decay. Hyperparameter transfer aims to make near-optimal hyperparameter settings consistent across model scale, so that large models can be optimized by proxy tuning their smaller, cheaper-to-optimize counterparts. While transfer principles are well-studied in the context of dense neural networks in language and vision tasks, they remain comparatively under-explored for graph neural networks (GNNs). We develop and validate a transfer parameterization for GNNs trained with SGD, Adam, and AdamW. Through theoretical scaling analyses and controlled experiments, we show that the proposed parameterization yields stable feature updates, learning rate transfer, and improved performance as width and depth increase. For SGD, we identify graph-dependent first-layer correction factors and show that their use can accelerate early training in graphs with sparse bag-of-words inputs. For Adam, we explore how different message passing normalizations affect early- and late-training transfer behavior, illustrating the importance of message passing normalization and advocating for an associated hyperparameter. For AdamW, we adapt a parameterization that allows for the joint transfer of weight decay and learning rate. Together, these results provide a practical recipe for scaling GNNs across a variety of learning tasks and training scenarios.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.27279

A latent control model for realistic rodent motion

Aidan Sirbu, Charles Y. Zhang, Yuanjia Yang, Bence P. Ölveczky, Talmo D. Pereira, Blake A. Richards

原文摘要

The development of embodied models of animal behavior is a major goal of the neuro-AI community. Embodied models will enhance our ability to make direct comparisons between biological and artificial agents. To make such comparisons effective, though, we need embodied models that actually move like animals. Previous work has developed virtual animal bodies and techniques for training realistic motion through imitation. However, to date, there is no system for embodied animal modeling that produces natural movements both during trained goal-directed behavior, and during exploration driven by random generation. Here, we introduce Simulated Control via Augmented Motor Priors for Embodied Reinforcement learning (SCAMPER), a system for training an embodied virtual rat model, enabling natural movements during both goal-directed behavior and random exploration. SCAMPER uses a variational architecture with two distinct priors, a motion prior for control and a behavior prior for exploration. We show that combining both of these priors allows SCAMPER to generate realistic behavior during exploration and exploit the task structure effectively. SCAMPER can also learn from sparse rewards as a result of the diverse movements induced by the behavior prior, allowing the system to model realistic sparse-reward tasks from experimental neuroscience. Altogether, SCAMPER provides the neuro-AI community with an effective new system for training embodied models of rodents on realistic tasks.

cs.AI · cs.LG

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