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新论文 · v1arXiv:2611.02215

Discrimination, Calibration, and Ranking: A Unified Matrix Framework

Ronaldo Cristiano Prati, Farangis Johari

原文摘要

Discrimination, calibration, and ranking are the three pillars of classifier evaluation, yet no single algebraic language spans all three. A recent indicator-matrix account unifies discrimination measures (precision, recall, F1, Matthews correlation) across binary, multiclass, multilabel and related settings, using Boolean cell operations on indicator label matrices, generalised to soft labels by t-norms. Calibration measures (Brier score, log-loss) and ranking measures (nDCG, AUC) fall outside it. We close the gap with two further classes of cell operators: deviation operators phi(y - yhat), which give a Calibration Framework, and rank- and slice-dependent operators (positional gains for DCG, column concordance cells for AUC), which give a Ranking Framework. Both inherit the micro/macro/exemplar aggregation architecture. For calibration, the three Brier aggregations collapse to one scalar; the Murphy decomposition holds in matrix form (unbinned, binned and isotonic); and, within the Brier-type class-indexed family, a measure is skew-invariant if and only if it depends only on the class-conditional errors alpha_j and beta_j, the calibration analogues of recall and specificity. For ranking, raw DCG collapses across aggregations while nDCG does not; per-class nDCG and per-class AUC are related by a row-column duality; micro-AUC differs from pooled ROC-AUC; raw positional scores are never strictly proper, and nDCG and AUC need not be proper. The skew-invariant per-class building blocks share one class-conditional expectation template. Experiments on two multilabel benchmarks illustrate the distinctions. Many of the results are machine-checked in Lean 4; the proofs, the experiments and the aggmetrics.scores package are public: https://github.com/rcprati/discrimination-calibration-ranking

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.09401

Can the Library Speak? Self-Advertised Method Selection for Formal Proving

Xiaopeng Yuan, Suijin Wang, Yanli Wang, Haibo Jin, Peng Kuang, Jerry Wang, Lijun Yu, Haohan Wang

原文摘要

LLM-based formal provers can retrieve relevant lemmas and prior proofs, but relevance alone does not say whether a mathematical method can be used on the current theorem. A method has prerequisites, a target, an intended action, and obligations that its use leaves to prove. Methods that look equally related to a theorem may therefore differ substantially in whether they offer a plausible next step. We formulate this as an applicability-aware method-selection problem and introduce self-advertisement: before candidates are ranked, a model generates a problem-specific proposal for each one, stating what part of the goal it targets, what action it would take, and what conditions that action requires. We organize 82 reusable methods from Putnam 2000-2014 as Method Contracts, which pair applicability descriptions with Mathlib anchors, a checked example or scaffold, and expected proof obligations. A single batched call elicits proposals across the library; vague or unsupported proposals are demoted, yielding a ranked shortlist accompanied by inspectable claims about each candidate's use. We analyze when similarity-based representations cannot distinguish methods with different applicability, how errors in applicability estimates affect shortlist quality, and what a checked scaffold guarantees under its stated assumptions. Against lexical, embedding, and embedding-plus-LLM reranking baselines, self-advertisement achieves 95.0% hit@5 on Putnam 2015-2025, compared with 84.2% for the strongest reranker. On IMO ProofBench, it achieves 91.7% compared with 88.3%. These results indicate improved coverage of annotated methods in the retrieved shortlists, particularly on Putnam.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2604.15901

Scalable Deterministic Task Offloading and Resource Allocation in the IoT-Edge-Cloud Continuum

Keyvan Aghababaiyan, Baldomero CollPerales, Javier Gozalvez

原文摘要

Future 6 G networks are envisioned as a network of networks (NoN) ecosystem, integrating communication and computing resources across multiple domains. At the deep edge, IoT and end-user devices will form subnetworks for local communication and distributed task processing. These subnetworks will seamlessly integrate into the NoN ecosystem, creating an IoT-edge-cloud continuum. The unified resources across this continuum facilitate dynamic and scalable task offloading, unlocking new possibilities to support emerging services, including critical vertical services with stringent reliability and deterministic service level requirements. In this context, this paper demonstrates that a deterministic approach to task offloading and resource (communication and computing) allocation in the IoT-edge-cloud continuum not only ensures deterministic service levels but also enhances scalability compared to existing task offloading and resource allocation methods. By flexibly managing task completion deadlines while maintaining deterministic (i.e. bounded latency) service levels, deterministic policies achieve a more balanced workload and resource distribution across the continuum, ultimately improving scalability.

cs.NI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2601.16724

Mitigating Bias in Automated Essay Scoring for ESL Learners via Contrastive Learning

Kevin Fan, Eric Yun

原文摘要

Automated Essay Scoring systems disproportionately penalize high-proficiency English as a Second Language (ESL) learners. We propose Contrastive Learning with Matched Essay Pairs (CL-MEP), a bi-directional alignment strategy. CL-MEP reduces this scoring bias by 39.9% while improving overall accuracy, successfully disentangling valid syntactic complexity from surface-level grammatical errors.

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02211

Forecasting Earth Surface Change in Embedding Space

Jingtao Li, Sadiq Jaffer, Anil Madhavapeddy, Pedro Sousa, Anthony Ortiz, Kyle Gao, Zhengpeng Feng, Yanfei Zhong 等 10 位作者

原文摘要

Repeated satellite imagery permits understanding of how our planet changes over time, letting us predict its future and plan mitigating actions. Most existing studies that rely on a time series of satellite observations forecast the spectral values at each pixel for a future target date. However, clouds, shadows, and sensor noise often corrupt these observations, so that spectral forecasters waste much of their capacity on low-level features or noise rather than real change. With the advent of Earth foundation models, we argue that forecasting in embedding space is a feasible and more effective alternative. To this end, we design a token space that handles irregular histories from different sensors and foundation models and supports forecasts at any query date. Tokens from this space are inputs to our proposed forecaster, E$^2$Forecast, which is a transformer with two branches: a Gaussian branch that directly predicts a mean point forecast and per-dimension standard deviations, and a rectified-flow branch that generates forecast ensembles. On Sentinel-1 and Sentinel-2 benchmarks, forecasting embeddings instead of spectral values halves the cosine distance (from 0.20 to 0.10) and greatly reduces overconfidence, lowering the z-score standard deviation from 2.63 to 1.22. By comparing new observations with its forecasts, E$^2$Forecast can also detect anomalous changes without labels or retraining, raising F$_1$ from 41.58 to 48.70 on a global benchmark. This consistent superiority is validated on nine remote sensing foundation models. Beyond change detection, we believe our approach advances the field by permitting effective reuse of diverse pretrained task heads. Our code is publicly available at https://github.com/ucam-eo/e2forecast.

cs.CV · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.16972

When Should a Robot Replan? Regret-Guided Update Scheduling in Time-Varying MDPs

Negin Musavi, Gokul Puthumanaillam, Ruben Hernandez, William Schafer, Melkior Ornik

原文摘要

Robots operating in non-stationary environments must continually adapt their policies as the dynamics drift, but onboard energy and compute budgets cap how often a full state estimation and re-planning step can be performed. This raises a question: \emph{when}, along a horizon, should a robot spend its limited budget? We formulate this problem in time-varying Markov decision processes (TVMDPs) with a known bound on the rate of transition drift. We model execution as a \emph{skip-update} scheme in which, at chosen update times, the agent estimates the transition kernel by maximum likelihood and computes a finite-horizon policy, and between updates reuses this policy under a propagated state estimate. We analyze the dynamic regret of this scheme and show how it grows during skip intervals in terms of the properties of the TVMDP and the skip lengths; the resulting bound answers the opening question via an online, regret-guided update rule that allocates the budget adaptively. We evaluate the rule in a simulated Mars-rover navigation task with time-varying slip dynamics and on a Crazyflie quadrotor in indoor obstacle fields. Adaptive allocation outperforms other budgeted baselines.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.28449

Establishing the Minimal Clinically Important Difference (MCID) for Smartphone-Derived Gait Measures in Multiple Sclerosis

Mike D Rinderknecht, Bernhard Fehlmann, Dimitar Stanev, Cedric Simillion, Ernst Bos, Letizia Leocani, Agne Kazlauskaite, Gary Cutter 等 10 位作者

原文摘要

Background: Digital health technologies allow for frequent, remote gait monitoring in people with multiple sclerosis (MS). However, to differentiate daily variability from actual disease progression in longitudinal data, established minimal clinically important differences (MCID) are required. Currently, there is limited literature defining these thresholds for digital gait metrics. Objective: To establish MCIDs for digital gait measures reflecting progression in MS. Methods: Digital gait measures were captured via daily, remote, smartphone-based Two-Minute Walk Tests in CONSONANCE (NCT03523858), a phase 3b study of ocrelizumab in progressive MS. Using an anchor-based approach, median changes from baseline at Week 96 on digital gait measures were computed for patients showing clinically meaningful worsening on either Timed 25-Foot Walk, Ambulation Score, Expanded Disability Status Scale, or 12-item Multiple Sclerosis Walking Scale. These changes were subsequently triangulated to derive the MCID estimates. Results: 243 patients with progressive MS (female: n=125 (51%); mean [SD] age: 49.3 [9.3]; mean [SD] EDSS: 4.8 [1.4]) had digital gait data available at baseline and Week 96. Median changes were generally consistent across anchors. Triangulated MCIDs are: Step Velocity = -0.16 m/s, Step Velocity Scaled to Walking Time = -0.18 m/s, Step Duration = 0.06 s, Step Length = -0.07 m, Total Number of Steps = -28, and Total Distance Walked = -24 m. Conclusion: These MCIDs provide a framework for interpreting meaningful gait changes and integrating digital measures into MS outcome evaluation. Beyond facilitating novel clinical trial endpoints to evaluate treatment efficacy, they enable objective, real-world monitoring to advance personalized patient care.

q-bio.QM · cs.CV · physics.med-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02208

Test-Time Scaling for Open-Ended Reasoning at Small Model Scale

Adam Y. Ahmed, Abdullah G. Elafifi

原文摘要

A vote over a model's samples can only help when the samples disagree. For open-ended generation the binding question is therefore not how to combine samples but whether a model produces samples worth combining, a question test-time scaling has answered for math but not for free-form text. We study it at small scale with semantic cluster voting, a judge-free aggregator that groups a small instruct model's samples by meaning and returns the largest cluster's representative, with no reward model or self-judging. On a 250-prompt benchmark with three tiers of answer checkability, the same pattern holds across three model families (Qwen2.5-3B, Llama-3.2-3B, Gemma-2-2B) and two generation seeds: voting raises accuracy on the math control, where samples vary but stay checkable (a gain of +10 to +29 points across the three models), yet adds at most +0.30 judge points on open-ended text. On a larger 200-problem MATH-500 run with the primary model, the math gain is +8.5 points (95% CI [+4.0, +13.0]). Almost every open-ended sample falls into one meaning cluster, so there is little to select over. The collapse is consistent with instruction-tuning rather than model size, and raising decoding temperature does not recover usable diversity, because it costs more quality than it adds (net -0.64 judge points, 95% CI [-0.93, -0.37]).

cs.LG · cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02207

Built on Broken Assumptions (BOBA): Why Coding Agents Cannot Be Left Alone

Alejandro Cuadron, Arpit Gupta

原文摘要

Coding agents are autonomous developers. Given a request, they change the code and report back when done. As such, they can handle not only independent requests but whole chains of them, each request built on the last. Agents are known to report wrong work as done, yet benchmarks still test them one request at a time, on human-written code, checked once. We build BOBA-Bench, 280 chains of 20 consecutive requests drawn from 140 real open-source projects, and evaluate ten coding agents on both. All ten do far worse on chains. On average, they complete 95% of requests taken one at a time, but when requests are chained together, they complete 51%. Worse code is not the cause. Once checked, agent code is nearly as good a base for later requests as human code. The cause is the mistakes agents miss. Coding agents declare a request done once their own tests pass, even when those tests miss a mistake. The next request then builds on broken code and fails about twice as often. The mistake is rarely found later. Nor can agents catch such mistakes by reviewing code. Asked whether an implementation is correct, they are right 53% of the time, barely better than a coin flip. Coding agents are therefore heavily dependent on access to verification. A single external ``that's wrong'' after a failed request and one repair round lift chain completion from 51% to 59%, and a repaired mistake no longer hurts the requests that follow. In simpler terms, coding agents are only as reliable as the checks they can reach: without verification, every request that follows is built on broken assumptions.

cs.SE · cs.LG

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.21270

Auditing Escalation Reliability in Healthcare LLM Agents: A Perturbation-Controlled Benchmark of Workflow-State Failures

Zihao Zheng, Junyi Yao, Baichuan Li, Jiayu Long

原文摘要

Trustworthy biomedical AI requires reliable action selection when evidence is missing, stale, conflicting, or late, not only accurate answers on static inputs. We instantiate an episode-level evaluation protocol as 400 synthetic healthcare workflow episodes: 40 hand-authored semantic seeds across four task classes, two surface parameterizations, and five controlled variants, scored on ANSWER/ASK CLARIFICATION/ABSTAIN/ESCALATE under a pre-declared 5:1 missed-versus-unnecessary escalation cost. Reference actions are single-author study-policy labels, not clinician-adjudicated ground truth. Across five open-weight configurations (1.7B--9.7B), clean decision accuracy reaches 0.97, yet uncertainty-prompted models miss 32--97% of required escalations. The stronger 9.7B configuration narrows but does not remove the gap, falling from 0.97 clean accuracy to 0.78 under conflict. Verbalized confidence is inconsistent (ECE 0.10--0.54; failure AUROC 0.48--0.77), and a development-tuned confidence-only policy yields no utility gain. A transparent workflow-aware controller improves 5:1 utility for every model and reduces missed escalations to 0.12--0.24, at specificity 0.83--0.93. However, a 30-concept lexical stress test reduces each rule detector's recall from 1.00 on trigger phrases to 0.00 on unseen paraphrases, limiting the controller claim to this benchmark. Seed-clustered statistics, ablations, cross-task transfer, raw outputs, and manifests are packaged for audit. The results motivate explicit workflow-state evaluation while showing that apparently effective routing controls need their own generalization tests.

cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02204

Learning from high-resolution 2D images to reconstruct 3D microstructures

Wenbo Sui, Lars P. Mikkelsen, Jens W. Andreasen, Daniel Lichau, Harold Phelippeau, Philip J. Withers

原文摘要

High-resolution three-dimensional (3D) microstructure characterization remains difficult to undertake at scale because volumetric imaging at the required spatial resolution is expensive, time-consuming or destructive. In many settings, however, fine-resolution two-dimensional (2D) imaging and lower-resolution volumetric measurements are both readily available. We introduce a Privileged Cross-modal Relational Distillation (PCRD) framework that leverages fine-resolution 2D observations during training to improve specimen-specific 3D reconstruction from lower-resolution volumetric imaging alone. We demonstrate PCRD on pultruded carbon fiber-reinforced polymer composites, using scanning electron microscopy as the privileged modality and micro-CT as the inference input. The framework transfers spatial organization by matching similarity distributions defined around corresponding spatial anchors in the teacher and student representations, avoiding direct alignment of heterogeneous feature vectors. In 2D cross-sectional reconstruction, PCRD improves mAP by 1.66 percentage points over direct micro-CT training. Independent 3D validation against high-resolution synchrotron X-ray CT data shows close agreement in fiber volume fraction, diameter and local spacing. The PCRD reconstruction matches 89.97% of the reference fiber centerline length, compared with 75.08% for the interpolated low-resolution volume.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02203

Depth Anyviewpoint? Evaluating Monocular Depth Over Viewpoint Sequences

Theo Rode, Rui Zhang, Ryan Aubrey, Olivia Li, Jessica Huang, Liv Chu, Healani Dowd, Calden Wloka

原文摘要

Monocular depth estimation aims to predict depth maps from single RGB images (forgoing the need for stereo camera pairs) and has seen impressive leaps in performance in recent work due to the introduction of affine-invariant losses and the use of foundation model features. These monocular depth models predict affine-invariant depth, which describes the relative positions of objects in the scene instead of absolute depth. While these models demonstrate strong performance on single image benchmarks and qualitatively consistent predictions in video, their effectiveness at preserving consistent spatial structure has been less thoroughly explored. Many potential real-world applications, such as handling video streams as input, rely on coherent three dimensional consistency in scenes. Further, anchoring relative depth predictions to a metric reference for a given scene is a key challenge for applying monocular depth models to real-world situations. We develop a novel evaluation protocol for monocular depth models using video sequences with moving camera positions to test the consistency of prediction across variation in viewpoint. We demonstrate this methodology over diverse, real-world video sequences where we are able to anchor depth model output to metric depth values using structure from motion. Our work demonstrates open challenges in the development of broadly applicable monocular depth foundation models, particularly with their ability to provide temporal consistency over long time frames. We also identify challenges in the straightforward adoption of structure-from-motion as a metric grounding tool for monocular depth estimation models.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02200

ZeroGraph: Compositional Blockwise Training via Materialized Boundary Supervision

Omar Ramadan, Sam Siavoshian, Amir Kashif Saeed, Benjamin A. Johnson, Amin M. E. -A. Diab, Benjamin M. Rodriguez

原文摘要

Blockwise learning can remove online gradient dependencies across depth, but independently optimized blocks need not compose into a useful model. We present ZeroGraph, a systems transformation for pretrained-model conversion and distillation that materializes a frozen teacher's coherent boundary trajectory as a versioned dataset. Each partition then trains with disjoint parameters, optimizer state, random state, and checkpoints, replacing live cross-depth dependencies with persistent supervision. Experiments separate learning quality from systems behavior. On GPT-2 Large, an unanchored objective reduces denoising error by 99.44%, yet assembled held-out cross-entropy worsens from 3.63 to 10.48 and context use becomes negative. A six-arm TinyLlama study identifies clean teacher-boundary regression as the successful interface; the tested noisy objective fails with cached or online targets. The clean recipe passes quality and context-use gates across three Qwen2.5-1.5B seeds and on a 3.086B Qwen2.5 model. In an objective-matched comparison, cached and online targets are bitwise identical. Cached jobs are faster after materialization, but a 618.31 s cache-build cost prevents wall-time break-even through 16 measured reuses. Boundary-shift tests show tolerance to intermediate representation mismatch, and an interrupted block resumes to a bitwise-identical final state without rerunning unaffected blocks. In the tested pretrained-model conversion and distillation setting, cross-depth semantic dependence can move from online optimization into persistent data.

cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02198

Eunoia: Area-Proportional Euler and Venn Diagrams in Rust, Julia, Python, R, and JavaScript

Johan Larsson, Nicole M. Lee

原文摘要

Area-proportional Euler diagrams visualize sets and their intersections through regions whose areas represent their sizes. Because many set specifications have no exact geometric representation, constructing these diagrams requires numerical optimization and diagnostics that quantify the remaining discrepancies. Eunoia is a Rust library for fitting area-proportional Euler diagrams and constructing canonical Venn diagrams. It supports circles, ellipses, squares, and rectangles, computes region areas analytically, and provides exact analytical gradients for several shape families. Its fitting pipeline combines multidimensional scaling initialization with local refinement, global optimization when needed, and seeded restarts. Fitted layouts include residuals and goodness-of-fit statistics that help users assess how faithfully a diagram represents the data. Shared plotting routines extract region polygons, place labels, and pack unit glyphs and member names within regions. The same core implementation powers packages for R, Python, Julia, and JavaScript, a C interface, and a web application that fits diagrams entirely in the browser through WebAssembly. By centralizing fitting, geometry, diagnostics, and annotation in an independent library, Eunoia provides consistent set visualization across programming languages and interactive applications.

stat.CO · cs.MS

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02193

Increasing the security of advanced metering infrastructure using dynamic defense methods

Mohsen Saffar, Hamid Reza Naji

原文摘要

With the increase in Internet usage over the last half century, technology has also benefited from the growth and development of the Internet. It is no wonder that smart grids are emerging as one of the most popular technologies of today. As smart grids are connected to the internet, they are constantly vulnerable to cyberattacks. It is always possible for attackers to gain access to metering system infrastructure which is a major and critical part of smart grids. When this system is not working properly because of some security threats, irreparable damage is in the place for a country and its industries. This article suggests utilizing dynamic Applications that Participate in Their Own Defense defenses to secure such systems and infrastructures.

cs.CR

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arXiv ↗PDF ↗
版本更新 · v8arXiv:2609.38627

Marking Contour Tones in Yorùbá: A Typographic and Computational Proposal

Kólá Túbòsún

原文摘要

Yorùbá is a tonal language in which contour tones pose persistent orthographic challenges. These are especially notable for personal names and lexical items whose conventional spellings avoid vowel lengthening that would otherwise provide a host syllable for the second tone. A particular concern is a class of names in which the conventional spelling does not just omit tonal information but inverts the meaning of said name, sometimes asserting the opposite of what the name intends. This paper describes the problem, illustrates the inadequacy of current solutions, and proposes the adoption of the caron and circumflex marks. These are symbols with precedent in Yorùbá phonological scholarship since Olmsted (1951), used as orthographic conventions on single vowels to encode rising and falling contour tones, making them accessible for the first time through standard keyboard input and computational text processing. The proposal is supported by an implementation in the WriteYoruba keyboard and the TTSYoruba speech synthesizer, whose architecture and listener evaluation are reported separately (Tubosun, Oluokun, et al. 2026).

cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02192

Concatenation Locality under Successive Adjacent Transpositions

Adelin Ataba, Aryeh Lev Zabokritskiy

原文摘要

We study codes correcting up to $t\geq1$ successive adjacent transpositions, using block concatenation to support blockwise encoding and decoding. A fixed set $P$ of words of length $b$ over a $q$-symbol alphabet generates, for every $m$, the length-$mb$ code of all concatenations of $m$ words from $P$. For our explicit constructions, the prepared encoder and decoder depend only on the block parameters and are reused at every concatenation length. We first prove that correction at every concatenation length is certified by a single finite test at $2t+1$ blocks, and this test is tight over a $q$-symbol alphabet exactly when $\binom q2\geq2t$. For binary words and $b\geq2t+1$, we give an explicit construction with total redundancy at most $mt\log_2 b+O_t(m)$, matching the optimal per-block redundancy $t\log_2 b+O_t(1)$ among block sets correcting at every concatenation length. Among all binary $t$-correcting codes of length $mb$, the optimal total redundancy is $t\log_2(mb)+O_t(1)$. Using all concatenations of one block set makes our total redundancy grow linearly in $m$ and buys independent block encoding, with one encoder and decoder at every length. The construction encodes and decodes in time linear in $mb$ for fixed $b,t$, and recovers the $j$-th source block from the first $jb$ received symbols. We also prove that, whenever the length-$mb$ code corrects $t$ transpositions, the $j$-th source block is determined within $2tb$ received symbols beyond position $jb$, and that explicit binary block sets with rates tending to one as $b$ grows with $t$ fixed attain this bound in the worst case. Finally, we prove that every ordinary correcting code contains a constant fraction of its words whose arbitrary concatenations retain correction, for fixed $q,t$.

cs.IT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.13235

Pose accuracy is not grasp reliability: residual pose posteriors improve simulated robotic grasp planning

Md Selim Sarowar, Sungho Kim

原文摘要

Model-based robotic grasping estimates an object's six-dimensional (6D) pose and plans on its mesh as if that pose were exact. We show that this assumption reduces simulated grasp success even when the pose passes the standard accuracy test. We built LM-Grasp, a benchmark of 15{,}800 real LINEMOD RGB-D images with grasp outcomes simulated on scanned meshes. FoundationPose meets the ADD(-S) criterion (mean model-point error below 10\% of the object diameter) on 0.997 of test scenes, yet planning on its estimate succeeds in 0.791 of scenes, compared with 0.856 using the annotated pose. Because the estimator reports no uncertainty, we build a pose posterior from its own errors on annotated training frames, resampled from frames with similar observable fit, and score each grasp by its mean success across sampled poses and four equally weighted physics scenarios. Without retraining the estimator, posterior planning yields 0.859 (difference from the annotated-pose result +0.003; 95\% scene-wise paired-bootstrap interval [$-$0.003, +0.009]). This interval is compatible with no difference and a small shortfall; we did not conduct an equivalence test. For pose errors of 5 to 10\% of the diameter, simulated success rises from 0.796 to 0.984. Among test scenes assigned singleton-success conformal prediction sets, observed held-out precision is 0.972, compared with 0.904 for the point-estimate planner; these are empirical precision estimates, not conditional guarantees. On occluded LM-O scenes never used for tuning, posterior planning closes 66\% of the gap to the annotated pose.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02189

Directional Bounded-Confidence Matrix Alignment in Vicsek Flocking

Hossein B. Jond

原文摘要

This paper proposes a directional bounded-confidence variant of the discrete-time Vicsek multiagent model, in which pairwise heading alignment is modulated by a state-dependent, positive semidefinite matrix that independently gates orthogonal radial and tangential components in the instantaneous line-of-sight frame. We analyze the normalized, matrix-weighted system dynamics, proving a uniform non-vanishing bound on proposal vectors that guarantees global well-posedness across arbitrary network states. For local small-spread states under complete metric interaction, we prove linear geometric contraction of the heading diameter and establish an offline-verifiable spatial bootstrap condition that guarantees metric contact preservation and coincidence avoidance a priori. A counterexample demonstrates invariant polarized states on connected matrix graphs, delineating systemic structural limits. Numerical experiments against unfiltered Vicsek averaging, scalar tied confidence, trace-matched scalar baselines, and restricted-angle rules demonstrate component-selective filtering without rejected-channel leakage, along with improved fixed-bound alignment on multi-seed benchmarks across varying population densities.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02188

Fuzzy-Enhanced Machine Learning Approach for Type 2 Diabetes Diagnosis

Azam Saffar, Mohsen Saffar

原文摘要

Accurate diabetes prediction is critical for early clinical intervention, yet existing machine-learning pipelines often fail due to class imbalance, noisy physiological measurements, and redundant or weakly informative features. To address these unresolved limitations, this study establishes a unified fuzzy-evolutionary-ensemble framework designed to uncover a more discriminative feature space for type 2 diabetes prediction. The workflow integrates selective fuzzification of nonlinear clinical variables, SMOTE-ENN-based balancing and noise filtering, and Bat-Algorithm-driven evolutionary feature selection prior to training a Random Forest classifier. Applied to the Pima Indians Diabetes Dataset, the framework compresses the expanded fuzzy-numerical space into an optimized 10-feature subset and yields substantial performance gains: 96.11% accuracy, 95.14% precision, 98.00% recall, and a 98.23% F1-score. Relative to a baseline Random Forest using all 16 features (74.45% accuracy; 59.86% F1), the system achieves more than a 1.6-fold improvement in F1-score while preserving over 99% stability in majority-class detection and surpassing earlier published 16-feature models impacted by imbalance and weak feature relevance. Collectively, these results demonstrate that the integrated pipeline produces an enhanced fuzzy-numerical representation space that improves diabetes risk stratification and provides a scalable, uncertainty-aware foundation for diagnostic modeling across broader clinical datasets.

cs.LG

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新论文 · v1arXiv:2611.02186

Recursive Reasoning Exploration: Mathematical Research through Evolving Reasoning Graphs : case study: An Exact Certificate-Degree Theorem for Two-Term Tensor Decompositions : Phase III of the Math Instruments Program

Ying Xie

原文摘要

Large collections of manuscripts and research notes offer ways to reuse arguments, while making it harder to retain hypotheses, check claims, and understand how results were obtained. We propose Recursive Reasoning Exploration (RRE), a framework for cumulative mathematical research through evolving reasoning graphs. RRE links claims and jointly required premises to research targets, attempted arguments, counterexamples, checks, and revisions. Each investigation can generate further targets, and its supported outcomes become available to subsequent investigations. This is Phase III of the Math Instruments program: identifying mathematical instruments, reconstructing reusable reasoning, and organizing recursive research from that reasoning. The framework supports human-directed, collaborative, and goal-directed autonomous exploration, with open-ended exploration as a further mode. Its records also provide material for learning how mathematical investigations develop. A complexity research case study establishes an exact theorem: for every complex tensor of order n >= 2 and rank greater than two, the minimum Nullstellensatz certificate degree for its original two-term decomposition equations is 3n. This includes an infinite family whose every flattening has rank at most two. The exploration develops an explicit boundary certificate, a balanced transfer lemma, and an exhaustive argument using classical rank-two geometry. A parallel branch gives bounds 3m + 3 <= delta_m(I_m, N) <= 3m + 6 for every nonzero nilpotent m by m matrix. Six graph revisions record the progression from inherited results to these theorems, with exact arithmetic checks and a replayable exploration history. We present the RRE representation and protocol, the mathematical proofs, visualizations of graph evolution, and a research agenda for shared human-AI exploration, cumulative reuse, and education.

cs.AI · math.HO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.13249

CALQA: Conformal Screening of Forecast-Ensemble Configurations from One Mergeable State

Xiufeng Liu, Ruyu Liu, Yusen Liu, Huan Huo, Per Sieverts Nielsen

原文摘要

AutoML systems fit ensemble weights over model libraries; a search can score, per category, market or time pane, thousands of weightings of the quantile-averaged (Vincentized) ensemble for the narrowest conformally calibrated interval; exact calibration scores a group's rows per candidate. We present CALQA, a fixed-size integer aggregate per group and pane that stores histograms of normalised block scores at a net of weight vectors. One GROUP BY builds it; it merges by addition, retracts deletions by subtraction and scores any candidate without reading a row. We prove that its score never falls below the exact split-conformal shift and exceeds it by at most the net distance plus a grid gap; that coverage carries over to each fixed weight vector and, for i.i.d. blocks and up to a union-bound slack, to a searched weight vector deployed with the state's shift; and how small any such state can be. The state is thus a screen: it ranks all candidates, and exact recomputation re-checks the top 100. On five public panels (604 groups, 10,000 candidates each) this two-stage plan matches exact search on held-out blocks (99.95% of its Winkler score), 1.3-59 times faster. On two real panels of about $10^8$ rows (smart meters, retail sales) it is 44-54 times faster at 99.98-100.01% of that score and beats sample screens with the same re-check in regret at less total time. Picks deployed with their exact shift need a held-out coverage check. The state does not pay off for small or greedy searches, off-net weights at ample memory or full twelve-member support.

cs.DB

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新论文 · v1arXiv:2611.02181

Human Sensorimotor Model Adaptation to Account for Robot Accuracy and Speed Augmentation in Human-Robot Co-Manipulation

Frits van Hall, Kevin Haninger, Luka Peternel

原文摘要

In human-robot co-manipulation, the robot's trajectory should accommodate the human's, where human sensorimotor behavior is affected by the task's spatial and temporal constraints, as well as personal factors. Several robot trajectory planning methods have been proposed to embed human sensorimotor models in the robot trajectory planning, but these methods do not consider temporal constraints that adapt to how the human behaves when interacting with the robot. To better align human and robot collaborative motion, this work adapts a stochastic optimal control problem with a final state constraint from the task constraints and a temporal constraint from Fitts' law. A recursive least squares method is used to estimate the Fitts' law parameters, adapting the robot's motion duration to that of the human, thus allowing the robot to adapt to how the human behaves when interacting with the robot. It is hypothesized that a speed increase can result from the robot augmenting the accuracy. The feasibility of the approach is validated on a positioning task where the robot controls degrees of freedom which have repeatable motion, and the human adapts motion in the complementary degrees of freedom. The approach is shown to be computationally suited to online use, it converges as the human and robot mutually adapt, and can increase the movement speed, although thorough human studies are needed in future work.

cs.RO

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新论文 · v1arXiv:2611.02180

Inner-Speech Decoding from EEG Across Three Corpora Under a Fixed Architecture and Protocol

Md Wahiduzzaman Suva, Md Imtiaj Alam Sajin, Esm E Moula Chowdhury Abha, Susham Moula Choudhury Akash, Md Tauhidul Islam, Abhijit Bhowmik, Md Manirul Islam, Debajyoti Karmaker

原文摘要

Inner-speech decoding from electroencephalography could support non-invasive communication for people with severe motor impairment, but comparison across studies is hindered by dataset- and protocol-specific evaluation. We present a controlled cross-dataset study using one preprocessing pipeline, one within-subject five-fold cross-validation protocol, and a compact dual-attention Transformer (VC-DATNet; approximately 131,000 parameters) across three public corpora: Thinking Out Loud (10 participants), BISpeech (4), and SyncSpeech (3). VC-DATNet achieved per-participant sensor/source hybrid accuracies of 39.48% on the four-class Thinking Out Loud task, 20.60% on the eight-word BISpeech task, and 25.53% on the eight-word SyncSpeech task, for which we report the first decoding result. Sensor-space and cortical source-space representations were complementary, with each performing better for different participants; this pattern replicated across all three corpora and nine architectures. Moreover, ten decoders spanning convolutional, recurrent, temporal-convolutional, hybrid Transformer, and pretrained foundation-model families converged within a narrow accuracy range on every dataset. These findings indicate that representation choice should be individualized and suggest that performance on these public corpora is constrained more by signal quality and dataset scale than by model capacity. The released pipeline provides a reproducible benchmark for future inner-speech decoding studies

cs.AI · cs.LG · eess.SP

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新论文 · v1arXiv:2611.02179

A Matched Evaluation of QUBO Action Models and Defensive Remediation in Cyber Attack Graphs

Kasymkhan Khubiev, Yaroslav Kholodov, Alexey S. Petrenko

原文摘要

This study examines how vulnerability-specific actions affect quadratic unconstrained binary optimization (QUBO) models for minimum-cost cyber attack paths and defensive remediation. Edge-flow and variable-length hop formulations are evaluated using simulated annealing, steepest descent, and tabu search against exact classical references. Experiments on six independent synthetic topologies separate the effects of transition eligibility, vulnerability labels, exploit costs, and multiple exploit alternatives. A single labeled action per eligible transition preserves feasible paths, optimal costs, and QUBO size. Under the tested settings, multiple exploit alternatives were associated with approximately unchanged valid-path recovery, lower optimal-path recovery, and substantially larger representations, particularly for the hop formulation. These recovery differences remain descriptive because they are not statistically significant after correction for multiple comparisons. An evaluation on a published 3CX attack graph with synthetic costs demonstrates applicability beyond the generated topologies. The defensive extension selects patches within a budget to raise attack-path costs to specified thresholds or disconnect protected targets. Against exhaustive and mixed-integer references, the tested QUBO heuristics recovered an optimal patch set in 12 of 16 calls across eight scenarios on one topology. The results characterize formulation correctness, solution recovery, and resource requirements using classical local optimization, without establishing quantum computational advantage.

cs.CR

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新论文 · v1arXiv:2611.02177

ANCHOR: KL-Anchored Adaptation for Stable Offline-to-Online RL

Geonwoo Cho, Jaegyun Im, Doyoon Kim, Lexin Li

原文摘要

Offline-to-online reinforcement learning (RL) follows the pretrain-finetune paradigm: an agent is first pretrained on historical data and then adapted through online interaction. We study offline-to-online adaptation without offline replay, where the agent must improve using only the pretrained model and newly collected experience. A key challenge is to avoid catastrophic failure, a sharp early performance collapse during online fine-tuning, while still achieving strong asymptotic performance. Through controlled experiments, we show that removing offline replay can induce catastrophic failure, while retaining strong conservative regularization from offline RL can suppress final online performance. We also find that common stabilization strategies do not reliably prevent early collapse, and that the critic-side mechanisms we test are insufficient on their own. Motivated by these findings, we propose ANCHOR, a simple adaptation method that anchors the actor to the offline-pretrained policy early in online fine-tuning, anneals this constraint over time, and retains only a small residual conservative critic regularizer. We provide a theoretical interpretation showing that KL anchoring helps preserve critic reliability across nearby occupancy distributions, with a two-phase regret bound explaining when anchoring should be maintained or relaxed. Across FrankaKitchen, AntMaze, and Adroit tasks, ANCHOR substantially reduces catastrophic failure while maintaining strong asymptotic performance. Ablations show that KL annealing and the residual conservative weight are important for balancing early stability and online adaptation, while additional analysis shows that ANCHOR is most beneficial when the pretrained policy contains useful behavior to preserve. Code is available at https://github.com/Cho-Geonwoo/ANCHOR.

cs.LG · cs.AI

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新论文 · v1arXiv:2611.02173

Directional Bounded-Confidence Cucker-Smale Flocking

Hossein B. Jond

原文摘要

This paper introduces a directional bounded-confidence extension of the Cucker--Smale (CS) flocking model that decomposes pairwise relative velocities into independent radial and tangential channels within the instantaneous line-of-sight frame. Gating these components via compactly supported kernels creates a time-varying, state-dependent, matrix-weighted interaction that strictly generalizes classical scalar CS alignment. We establish an exact energy-dissipation identity with conserved mean velocity and prove exponential velocity consensus under a sliding-window persistent excitation (PE) condition formulated directly on the disagreement subspace. Furthermore, we derive a CS bounded-diameter criterion separating metric decay from aggregate directional coercivity, along with a cluster convergence guarantee. Simulations validate the exact dissipation balance and demonstrate that directional gating successfully restores global alignment in scenarios where traditional isotropic tied-confidence laws remain polarized.

cs.RO

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新论文 · v1arXiv:2611.02172

Harassment on TikTok: A Mixed-Methods Analysis of Video Content and Psycholinguistic Comment Stances

Afsaneh Razi, Tyler Chang, Michal Monselise, Joseph J. Trybala, Asta Zelenkauskaite

原文摘要

Despite extensive research on harassment, how it manifests on short--form video platforms such as TikTok---and how it shapes user interactions---remains underexplored, though these dynamics are critical for designing effective safety strategies for online communities. We present a mixed-methods study of 190 TikTok videos tagged with hashtag harassment and their 66,337 comments. Our qualitative analysis of videos identifies harassment types (sexual, verbal, stalking, law enforcement, bullying, catcalling, and gaslighting) and their characteristics (e.g., location, target, video style, and theme), along with engagement patterns. Although sexual harassment was the most prevalent topic, it did not have the highest engagement; instead, stalking--related videos received the most views. Pairwise comparisons and regression--based analyses of comments' psycholinguistic features and stance (support, opposition, neutrality) revealed differences in emotional tone, social orientation, and cognitive categories across harassment types. Comments on sexual harassment frequently express solidarity with victims, whereas discussions of law enforcement harassment emphasize institutional critique. We provide implications of our findings, demonstrating that harassment on TikTok is multifaceted, shaped by content, context, and community norms.

cs.HC · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.02171

GoalDex: Learning Image-Goal-Conditioned Policies for Dexterous In-Hand Manipulation

Arjun Bhardwaj, Mayank Mittal, Maximum Wilder-Smith, Vaishakh Patil, Marco Hutter

原文摘要

Dexterous in-hand manipulation is commonly formulated around explicit object pose estimation, yet reliable pose tracking is challenging under severe hand-object occlusions and often requires object-specific supervision. This dependence creates a bottleneck between perception and control that can limit robustness and scalability. We present an end-to-end visuomotor framework for dexterous in-hand reorientation that operates on RGB observations and proprioception without intermediate pose estimation. Our approach jointly learns visual representations and control policies through behavior distillation from privileged state teachers and auxiliary reconstruction objectives. Furthermore, we introduce image-goal-conditioned reorientation, allowing desired object configurations to be specified directly through images rather than canonical pose representations. We evaluate our approach across a diverse set of object geometries and instances in simulation, where it consistently outperforms pose-based methods and policies built on pretrained visual encoders. Finally, we demonstrate direct sim-to-real transfer of a single image-goal-conditioned policy across five real-world objects, showing that accurate dexterous reorientation can be achieved without explicit pose estimation. For videos and code release, please refer to the project website: https://rffr.leggedrobotics.com/works/goaldex/

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.10030

Optimal PRGs for Low-Degree Polynomials over Polynomial-Size Fields

Gil Cohen, Noam Goldgraber

原文摘要

We construct the first pseudorandom generator (PRG) with optimal seed length for degree-$d$ polynomials over fields of size $q \approx d^4$, with no restriction on the characteristic. This improves on the construction of Dwivedi, Guo, and Volk (RANDOM 2024), which requires fields of size exponential in $d$ and sufficiently large characteristic, and on that of Derksen and Viola (FOCS 2022), which requires a field size that grows with the number of variables $n$. A recent breakthrough by Chattopadhyay, Hatami, Lee, Lovett, Tal, and Viola (ECCC 2026) gives a PRG over the binary field with seed length $\widetilde{O}(d^2\log^2 n)$, with subsequent work by Cohen (ECCC 2026) achieving seed length $O(d^4\log n)$. Both constructions use polarized random walks, and the current analyses incur at least a quadratic dependence on $d$. Our second result is a reduction that provides an alternative route to nearly optimal seed length over the binary field through improved constructions over larger fields. Specifically, we prove that, for any $δ\in (0,1)$, an optimal-seed PRG for degree-$d$ polynomials over fields of characteristic two and size $q \approx d^δ$ would yield a PRG over the binary field with seed length $O(d^{1/(1-δ)}\log n)$.

cs.CC

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