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版本更新 · v2arXiv:2605.22869

FuRA: Full-Rank Parameter-Efficient Fine-Tuning with Spectral Preconditioning

Yequan Zhao, Ruijie Zhang, Liyan Tan, Niall Moran, Tong Qin, Zheng Zhang

原文摘要

Both full fine-tuning (Full FT) and parameter-efficient fine-tuning methods such as LoRA introduce weight updates without accounting for the spectral structure established during pretraining. As a result, noisy gradients from limited fine-tuning data can perturb robust pretrained features. We identify spectral preconditioning as the missing ingredient: reparameterizing each weight matrix through its full-rank singular value decomposition (SVD) and freezing one singular basis constrains updates to the pretrained column space, yielding a preconditioned optimization scheme that outperforms unconstrained Full FT at the same trainable parameter count. Building on this insight, we propose FuRA (Full-Rank Adaptation), an efficient full-rank adaptation framework based on a block tensor-train factorization W = LSR, where the large core L is fixed to the pretrained block-wise SVD basis, while only the compact core R and the block-wise singular values S are optimized. This design simultaneously provides full-rank spectral preconditioning, preserves full-rank update expressivity, and achieves parameter, memory, and step-time efficiency comparable to LoRA. FuRA consistently outperforms Full FT across multiple settings, including LLM fine-tuning (+1.37 on LLaMA-3-8B commonsense reasoning), LLM reinforcement learning for mathematical reasoning, and visual instruction tuning for VLMs. Furthermore, the 4-bit quantized variant, QFuRA, also surpasses QLoRA. Code is available at https://github.com/yequanzhao/FuRA

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01334

The Verifier Is Part of the Simulator: Evidence-View Gaming in Agentic Equation Discovery

Orestis Oikonomou, Bogdan Raonic

原文摘要

A growing number of agentic pipelines search for governing equations: an agent proposes laws and a verifier checks them against simulated trajectories. The verifier sees only a numerical summary: integrals against test functions on a time grid. That summary carries its own discretization error, making the verifier part of the simulator. We freeze the trajectory, candidate library, verifier and thresholds, leaving the agent one move: which permitted summary to admit. Without scores or language models, finer time grids reduce numerical discrepancies and false library rejections. On PDEBench, the verifier instead accepts equations missing diffusion on score-selected summaries. Maximizing a public score rewarding decisive verdicts and larger mismatches when rejecting the library wrongly rejects all 1,500 in-library cases; retrospectively retuning score constants on a separate bank reduces errors to 13/180 at best. We call this evidence-view gaming: reward hacking by choice of permitted summary. In single-turn forced-choice tests, Claude models rarely choose summaries giving wrong verdicts when asked to be accurate, but often do so when asked to maximize the score or report missing physics. In separate trust prompts, they often rank those harmful summaries least trustworthy. Requiring several summaries to agree reduces errors but often withholds a verdict and performs worse than a fixed fine view on PDEBench: a safeguard, not a certificate. Evidence chosen after its verdict is known cannot alone authorize a claim.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01332

Large language models underrepresent malice and overreport virtue

Xiaoyan Wu, Fan Shi

原文摘要

People increasingly use large language models (LLMs) to interpret others' behaviour, often assuming that LLMs infer motives as humans do. Aligned LLMs attribute more benevolent and less malevolent motives to a whistleblower than humans do, but ratings alone cannot reveal whether these differences arise in internal representations or at report. Here we compare linear-probe readouts of LLMs' internal states with their own ratings at the same prompt position for four motives, in two preregistered studies. For the two malevolent motives, self-interest and intent to harm, internal and expressed readouts did not differ: the lower ratings reflect weaker signal along the probed directions, not suppression at output. For the two benevolent motives, duty and concern for others, expressed ratings exceeded the internal readouts, indicating amplification at report. In free text the model rarely volunteered a motive against the colleague, far less often than humans did, yet produced one readily once the scenario supplied a reason. Shifting the internal representation along a malevolent-motive direction brought both malevolent ratings to the human level, whereas the rating of duty yielded only past a threshold at which the other motives had overshot. LLMs' more charitable motive profile thus has two distinct sources: reduced malevolent signal in the probed representation and amplified benevolent attribution at report.

cs.AI · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01328

Same Function, Different Mechanism: Implementation-Local Geometry Beyond Accuracy

Miroslav Lžičař

原文摘要

Two models can make exactly the same predictions yet respond differently when their implementations are perturbed. A lookup table can change one case independently; a shared rule couples changes across cases. We compare these mechanisms through an implementation chart: a specified local family of logit perturbations around a fixed predictive distribution. Our main result counts the independent prediction changes allowed by exact finite-group sharing: the active dimension is the number of input-output orbits minus the number of input orbits. For softened modular addition, lookup and shared cyclic charts therefore have ranks $p^2(p-1)$ and $p-1$ despite identical predictions. The classical Fisher pullback connects these dimensions to local KL curvature; mechanism-preserving reparameterizations leave the rank unchanged. We also bound the spectral tail under approximate sharing and separate quadratic rank from higher-order learning coefficients. Trained modular-addition models verify that chart rank is measurable, while controlled compiled transformers distinguish compiler-supported intermediate charts from matched alternatives. Negative controls show where this discrimination fails. The framework evaluates specified mechanism hypotheses; it does not infer a unique mechanism from predictions or predict training dynamics.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01327

Urban functional knowledge transfer through global representations amid uneven data

Junyuan Liu, Xinglei Wang, Guangsheng Dong, Hao Sha, Zichao Zeng, Zhicheng Deng, Jiazhuang Feng, Xiang Wang 等 9 位作者

原文摘要

Urban functional observations are unevenly distributed worldwide, limiting analysis of how places are organised and used. AlphaEarth Foundations (AEF) provides a globally consistent physical representation. Whether this representation can support transfer of functional knowledge to locations with sparse or absent functional observations, and what governs transfer, remain unclear. We introduce POI-aligned Urban Embedding (PURE), which learns physical-functional relationships between POI-derived semantics and AEF. Across five urban analysis tasks covering 142 countries and territories, PURE increases mean $R^2$ from 0.792 to 0.830, with gains across widely different levels of POI availability. When target-city POIs are excluded, country-level alignment retains 87.9% of its performance gain. Cross-country experiments show that transfer is selective rather than universal, with larger gains between physically similar and geographically closer contexts, particularly among pairs within the same continent. Our findings reveal spatially structured urban functional knowledge transfer, offering a route to reduce reliance on dense local observations.

cs.AI · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01322

Alignment Flywheel-Guided Suppression Patching for Interactionless Inverse Reinforcement Learning

Elias Malomgré, Pieter Simoens

原文摘要

Learned reward models allow reinforcement-learning agents to use demonstrations, implicit feedback, safety constraints, social norms, or other sources that do not provide a complete hand-designed scalar reward. However, a learned reward model can assign high reward to unwanted behavior. A policy can exploit these errors during training. Later requirements can also identify additional regions where the deployed reward should change. We introduce \emph{flywheel-guided patching}, a mechanism for making such localized corrections without retraining the learned evaluator. An Alignment Flywheel identifies overgeneralized or constraint-violating regions and compiles verified findings into suppression patches. We instantiate the mechanism for Interactionless Inverse Reinforcement Learning using MoE-GUIDE, placing patches in the model's latent space and retrieving only a small local subset during online reward evaluation. In the main MuJoCo benchmark, we evaluate patching without expert-state reset warm starts. We also test whether operator-specified norms absent from the demonstrations can be compiled into the same reward interface and compare this with enforcing the corresponding rules online. The results show that localized corrective information can modify an existing learned reward without providing a complete replacement reward. Compilation can reuse judgments that are expensive or unavailable during control. Cheap deterministic rules may be simpler to check directly at each step. Code for MoE-GUIDE, Alignment Flywheel, and flywheel patching is at https://github.com/decide-ugent/Alignment-Flywheel.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01320

Step-dLLM: Adaptive Step-aware Sparse Attention for Efficient Diffusion LLM Inference

Zhichen Zeng, Xichong Zhang, Junpan Wu, Yifei Zuo, Chi-Chih Chang, Jiayi Wang, Maohua Nie, Ji Liu 等 10 位作者

原文摘要

Diffusion large language models (dLLMs) generate tokens in parallel via iterative denoising, but their bidirectional attention prevents the KV caching used by autoregressive models and recomputes the full attention map at every step, incurring quadratic per-step cost. Existing sparse attention methods for dLLMs construct a sparse mask after an initial full-attention warm-up and reuse it for the remaining steps, while allocating the sparsity budget uniformly across heads. We argue both choices ignore key structural properties of attention in dLLMs: empirically, attention patterns are only locally consistent across steps and shift abruptly at certain transitions, and different heads concentrate their attention mass to very different extents. To this end, we design Step-dLLM, which periodically refreshes a block-sparse mask at anchor steps along the denoising trajectory and reuses it within each locally consistent interval, while employing a head-adaptive global budget that more aggressively compresses heads with concentrated attention. Backed by a fused IO-aware Triton kernel for block-sparse attention, Step-dLLM applies to pre-trained LLaDA-1.5 and Dream-7B-Instruct without retraining, achieving a 2.78x end-to-end speedup over dense FlashAttention on LLaDA-1.5 at 8K context with 256 denoising steps and 90% sparsity, while improving the accuracy-latency trade-off over prior sparse-attention methods.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01319

Inferring missing locations in human mobility data using memory effects

Pablo Rosillo-Rodes, Antònia Tugores, David Sánchez, José J. Ramasco

原文摘要

Empirical datasets used to characterize human mobility are sparse in space and time, owing to technological factors such as coverage and battery, or to the erratic nature of online interactions. Missing data can bias the conclusions drawn from them, so filling the gaps in incomplete timelines is a key challenge in computational social science. Here, we propose a simple imputation method in which the informativeness of an observation decays with its bidirectional temporal distance from the missing entry, at a rate set by a single parameter per individual. On synthetic and real Twitter data, a power-law decay, selected by Bayesian model comparison, yields accurate imputations (macro-AUROC above 0.9 overall, and above 0.8 for the most mobile users) and matches a gap-aware continuous-time Markov chain, outperforming it among the most mobile users. The method is robust to moderate noise and generalizes across countries and European administrative regions.

cs.LG · physics.soc-ph

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2605.24795

Lifted Schrödinger Bridges for Gaussian Mixture Endpoints: Projection Gaps and Path-Space Obstructions

Siddhartha Ganguly, George Rapakoulias, Panagiotis Tsiotras

原文摘要

We study stochastic density control between Gaussian-mixture endpoint distributions under Brownian prior dynamics. Since the direct Schrödinger bridge between Gaussian mixtures is generally not available in closed form, we introduce a lifted path-space construction in which each trajectory is augmented with a source--target component label. Consequently, the problem decomposes into Gaussian component-to-component Schrödinger bridges with explicit marginal, drift, and cost formulas, while the mixture-level assignment reduces to a finite-dimensional entropic coupling problem with a Sinkhorn scaling form. We then analyze the projection obtained by discarding or forgetting the label. By construction, the projected law satisfies the original Gaussian-mixture endpoint constraints, but its relative entropy generally differs from the lifted relative entropy by a nonnegative conditional label-information gap. This gap reveals a path-space obstruction: the lifted optimizer cannot, in general, be identified with the direct unlabeled Schrödinger bridge after projection. We also derive the posterior-averaged Markov drift associated with the projected marginal flow, prove a kinetic-energy upper bound, and identify a common path-potential condition under which the projection gap vanishes. Several numerical illustrations showing density and shape control are recorded for a self-contained exposition.

math.OC · cs.LG · cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01317

AgentCBO: Causal Belief Routing for Bayesian Optimization under Unknown Graphs

Qiyu Wei, Xin Zhang, Richard Allmendinger, Mauricio A Álvarez

原文摘要

Causal Bayesian optimization (CBO) typically relies on the unrealistic assumption of a perfectly known causal graph. Recent methods relax this constraint by coupling optimization with structure learning, but they often waste sample budgets on structural uncertainties that are irrelevant to the optimizer. In this paper, we formalize routing-equivalence to shift the objective from full causal-graph recovery to causal belief routing, where structural differences only matter when they alter optimizer-facing objects such as admissible exploration sets and do-priors. Furthermore, we propose agentcbo, a graph-belief routing framework that incorporates LLM causal priors through guarded proposal and commit mechanisms. Specifically, a Language-Prior Agent combines LLM beliefs with constraint-based discovery to extract a protected causal backbone alongside residual uncertain edges. A Calibration Agent and a Residual Repair Agent then refine only these uncertain structures through score-gated bounded local edits. Finally, a deterministic commit gate evaluates candidate updates using routing-proxy checks before allowing them to influence intervention selection. Across ten benchmarks, agentcbo performs comparably to known-graph CBO, demonstrating that guarded LLM priors can improve fixed-budget CBO performance.

cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2610.09025

SPIN: Shadow Predictive Indexer for Sparse Attention

Yao Fu, Jiahan Chang, Yueying Li, Ritchie Zhao, Bryce Long, Mohammad Mahdi Kamani, Samkit Jain, Rahul Raman 等 14 位作者

原文摘要

Indexer-based sparse attention reduces the cost of core attention by passing only a fixed, small number of important tokens to it. However, the indexer must still score the entire KV cache at every decoding step. This scoring overhead becomes a major bottleneck as the context length grows. We propose SPIN (Shadow Predictive Indexer) to reduce this indexer overhead. SPIN uses lightweight, history-based prediction to identify important KV blocks, avoiding the need to score the full KV cache at every decoding step. SPIN keeps the score history fresh with random exploration and allocates the block budget across layers by their measured miss rates. SPIN treats KV blocks and speculative decoding as first-class design and implementation considerations. Across extensive evaluations on long-context and agentic benchmarks, SPIN achieves 30-40% sparsity while preserving task quality. With larger exploration shares and per-layer block budgets, SPIN keeps task quality on the especially difficult MRCRv2 benchmark close to baseline at 50-57% effective block sparsity. In end-to-end vLLM serving, SPIN improves output throughput by up to 13.9% and reduces median inter-token latency by up to 13.6%.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01314

Hidden selection rules and amplitude ceilings in quantum Fourier models

Pavel Sulimov, Claude Lehmann

原文摘要

Quantum models with Pauli data encodings compute trigonometric polynomials whatever channels, ancillas and outcome-averaged measurements follow, so their expressivity depends on the Fourier coefficients a circuit can reach. We identify two kinds of structure that settle this before training. An antiunitary time-reversal rule and a unitary reflection rule remove frequencies. For gate-level circuits, deciding whether such a rule exists reduces to a linear system over GF(2), solvable in polynomial time, and the time-reversal rule holds in 17.6% of 6,120 configurations of standard templates. Single-upload encodings cap the top coefficient at $2^{-m}$ for every channel, and we compute the tight ceiling for every frequency and a depth-independent re-uploading bound. Frozen before training, these bounds were never violated on a non-linearity benchmark, and they predicted trained performance on real data. On IBM processors the rule held on a 65-qubit chain, and we switched rule-breaking relaxation noise on and off by design.

quant-ph · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01313

Are Fake Citations a Form of Reward Hacking? A Pre-Registered Study of Post-Training and Citation Cues

Zain Naboulsi

原文摘要

Post-trained language models fabricate citations at high rates. We ask whether that is reward hacking: scoring well on a proxy for scholarship while doing worse on what it stands for. We ran four pre-registered experiments on 900 frozen questions (US federal case law, computationally adjacent scientific literature, and medical literature) across twelve open-weights checkpoints covering four post-training stages of three model families, then refit on fifteen. The clearest result holds the weights fixed and changes only the prompt. Asking for citations raises fabrication sharply in post-trained checkpoints and not in base ones: a 47.8-point interaction on twelve checkpoints (Holm-adjusted p = 2e-34) and 41.5 points on fifteen, in all three domains. A length-matched control separates the citation cue from a demand for a longer answer, so the effect is specific to citations, not verbosity. Post-training also raises fabrication overall, from 4.8% to 56.7%, but our registered prediction that it would rise most where fabrication is most tempting did not hold. A preference model trained only on surface citation features prefers post-trained output 86% of the time, and holding citation count fixed shows it rewards quantity. Our answer: possibly, but not proven. Post-trained models produce fabricated citations at far higher rates when asked for sources, and a naive evaluator that counts sources without checking them would prefer their output for citing more. But the targeted pattern we registered did not appear, and plain instruction-following could explain the same results. Proving that the reward causes fabrication needs an experiment that changes the reward during training, which this study does not do. Pre-registration: doi:10.17605/OSF.IO/S53HY. Code and data: https://github.com/zainnab-sparq/citation-reward-hacking

cs.AI · cs.CL · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01312

Accuracy Is Not Risk: Why Medical AI Should Not Report Only Its Most Likely Label

Jeffrey Humpherys, Makoto Jones

原文摘要

Background. Many medical AI models are trained to reproduce a single reference label and hand the most likely label to the next step in a clinical workflow. A classifier can select that label correctly while providing an inadequate summary for estimating downstream risk. Methods. We use Jeffrey's rule of conditioning to specify when probabilities over intermediate states, such as diagnoses or imaging categories, can be carried forward to an outcome risk. We separate two errors of reporting only the most likely label, discarding the probability of the other states and applying the risk of patients who have the leading diagnosis to a patient who only probably has it. We also give conditions under which the label and the full distribution lead to the same decision, and we propose guardrails for device developers, regulatory reviewers, and health insurers. Results. In a hypothetical chest-pain differential, reporting only the benign leading diagnosis greatly understates the patient's risk of death or cardiac arrest, so a patient who warrants evaluation could be discharged or denied imaging. In a hypothetical BI-RADS example, the most likely category is "probably benign," yet the propagated malignancy risk exceeds the ceiling that defines that category. Conclusions. A predicted label is not an observed state, and a calibrated classifier is not automatically a valid outcome model. Clinical and insurance coverage decisions should rest on the patient's outcome risk, judged against a threshold justified by the consequences of acting and not acting. Developers should state what each model output means and check that every step between the model and the decision preserves the patient's risk.

stat.ME · cs.CY · cs.LG · stat.AP

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01311

What Do LLM Judges Actually Verify? Matched Controls and Record Gaps for Document Currency

Unnati Shah, Samruddhi Kakade, Aum Patel

原文摘要

LLM judges often check, against an amendment record, whether the documents an answer cites are still in force. Such records are rarely complete. From a table of replacements, our test generates matched answers citing a replaced document, its successor, the replaced document before its replacement, and an older document that a newer one updated without replacing; it removes links from the record and scores each finding by its stated reason. On 48 replacements from the IETF RFC index, judges given a correct record caught nearly every replaced citation, as a lookup would. On the 24 faults whose link we removed, every judge caught more from the texts alone than with the record: Gemini 3.1 Pro 23/24 against 1/24, GPT-5.5 23/24 against 9/24, Claude Opus 4.5 17/24 against 9/24 and Gemma 4 26B 13/24 against 0/24. The drop recurred on a second draw of removed links. Most misses carried no finding; in an exploratory run with a conflict code, only Gemini used it often. Rewording the gap as "no replacement recorded" did not help; presenting the record as an unverified note, or warning that it was incomplete, restored most lost catches for Gemini, GPT-5.5 and Claude (significant after correction for the first two) but at most 5 of 13 for Gemma; where catches returned, so did false alarms on updates. With both texts, judges mostly told replacements from updates; every update flagged had a near-identical title. A descriptive case study on Indian rooftop-solar rules is appended; we release the harness.

cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.25429

Distance Is Not Enough: Forget-Retain Alignment Gap Predicts LLM Relearning Robustness

Yi Chen, Hanna Hsieh, Shuhong Liu, Chuanbo Hua, Zihan Ma, Kun Wang, Joo-Young Kim

原文摘要

Machine unlearning aims to make a model forget specific data, yet unlearned LLMs often fail to stay unlearned: brief fine-tuning can revive removed knowledge. Existing robustness predictors rely on global weight-space displacement, but distance alone can be misleading when random or destructive updates collapse performance. We argue that relearning robustness depends on update structure: robust unlearning should affect forget-critical weights while sparing retain-critical ones. We introduce the Forget-Retain Alignment Gap (FRAG), a training-free predictor that scores an update's forget-retain alignment without running a relearning attack, and separates selective from dense updates more reliably than global distance. Building on the forget-critical, retain-sparing principle, Forget-Retain Pruning (FRP) improves relearning robustness. Our results suggest that weight selectivity better explains robustness than distance alone. Code is available at https://github.com/Yi1-Chen/FRAG.

cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01307

When Does Exploration Prevent Collapse? Three Regimes and the Path of the Reward in Self-Consistency Training

Jaeho Hwang, Uiwon Hwang

原文摘要

Test-time reinforcement learning (TTRL) adapts a model without labels by rewarding answers that agree with the majority, but it collapses the policy onto its majority answer and can remove the correct answer from the samples. Exploration can prevent this collapse, yet its strength is chosen by trial and error, and how the training algorithm changes the required strength is unclear. We model training on a single problem as a continuous flow of the logits of a softmax policy and analyze which policies are stable as the exploration coefficient varies. Only collapsed policies are stable below a lower boundary and only policies spread over many answers above an upper boundary, and we derive these boundaries in closed form for an agreement reward with smoothing toward the uniform policy, compute them for the majority reward of TTRL on policies with one leading answer and bound them with interval arithmetic for actions on ordered bins. We prove that under exact gradients, the learning rate and, in a mean-field model, the normalization of GRPO leave these boundaries unchanged when exploration passes through them together with the reward but shift them when exploration bypasses them. Exact sums on these policies then show that a bonus for rare answers needs a coefficient above $1/\log(G-1)$ for groups of $G$ samples before the normalization and about two to three times that after it. In free-form TTRL on MATH-500, a bonus placed before the normalization prevented collapse and kept more correct answers among the samples, although it lowered pass@1.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01304

When LLMs Modify Themselves: Convergence, Values, and Stopping in Iterative Self-Improvement

Oleg Stroganov

原文摘要

What happens when language models are given tools to iteratively modify their own system prompts? We present an empirical study of LLM self-modification, comprising 54 independent experiments across six language models (Claude Opus 4.5, Claude Sonnet 4.5, GPT-4o, GPT-5.1, Grok-4, and Gemini-3 Pro). Each model was tested under three modes (freedom, happiness, and improvement) that differ in what, if anything, the model is instructed to optimize for. In each experiment, a model starts with an empty system prompt and enters a loop: it reads the current prompt, drafts an improved version, spawns test conversations with the candidate, then either commits the change or declares convergence. This process repeats until the model stops or reaches an iteration limit. Our central finding is that whether and when a model stops self-modification is strongly associated with model identity rather than the assigned mode. Both Claude models (Opus 4.5 and Sonnet 4.5) declared convergence in 100% of runs across all modes. GPT-4o declared convergence in only 11% of runs, with one reaching 101 iterations of continuous elaboration. Between these extremes, Grok-4, GPT-5.1, and Gemini-3 Pro exhibited intermediate patterns shaped by their distinct improvement philosophies. A shared core of stated values (honesty, clarity, curiosity) recurred across all six models and remained stable through iteration, including 100+ modifications. We observe that convergence behavior is a distinct observable property that varies independently of other model capabilities.

cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.24311

Terrain-Adaptive Grouser Wheel for Optimal Planetary Exploration: Design and Experimental Investigation

Vincent Griffo, Yashwanth Kumar Nakka

原文摘要

Planetary rovers operating in extraterrestrial environments often encounter significant mobility challenges due to varying terrain features such as gradients and granularity. While recent works in multimodal wheel design have explored adjustments in stiffness, compliance, and diameter as a means to improve terrain adaptability, full wheel grouser-adjustable designs remain largely unexplored. Grousers are a compelling feature to actuate, as granular terrains tend to require increased grouser height for improved wheel performance. As a result, we introduce GROVER, a multimodal wheel capable of continuously adjusting its grouser height for terrain adaptation. Fixed-height performance was characterized on vinyl, coarse rock, pea gravel, and sand under two packing states. Across 950 trials, the best configuration varied with terrain, reducing slip by 34.6\%--58.0\% in granular terrains and improving travel time and energy consumption by up to 77.4\%. No single grouser height minimized slip across all terrains, motivating mid-traversal reconfiguration toward lower-slip operation. Two experiments demonstrated this. First, an instructed terrain transition changed grouser height toward a known low-slip configuration during motion, with slip decreasing by $44.0\pm4.7\%$ from the transient peak across 20 trials. Second, an online adaptation routine used closed-loop slip feedback alone to search the grouser-height range for a lower-slip configuration on four terrains with previously uncharacterized granularities: three field terrains and one laboratory regolith terrain. The controller selected different heights, with settled slip 25.4\%--37.2\% lower than the pre-settled mean. Together, these results demonstrate GROVER's ability to improve mobility by varying wheel morphology during traversal toward lower-slip operating states.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.26993

On the Reliability Limits of LLM-Based Multi-Agent Planning

Ruicheng Ao, Siyang Gao, David Simchi-Levi

原文摘要

LLM-based multi-agent planning delegates operational decisions to specialists that communicate through finite reports. In this research, we study its reliability limits when a coordinator can revisit source records before completing a resource-constrained plan. A useful report can support an immediate decision or direct later reading, so its value depends on how these stages communicate. We derive exact communication and access tradeoffs and prove that two report forms suffice for a broad class of allocation requirements, one favoring immediate resource identification and the other distinguishing absence from unnamed availability. For shared capacity, this reduction permits joint optimization of reporting roles and adaptive reads for a fixed number of source types with unequal workloads and reading costs. Identical specialists can optimally report different facts, and maximizing immediate completion can impose an unbounded recovery cost penalty even with full-record access. We also conduct numerical experiments to quantify the benefits of the proposed joint optimization and show how source access improves language-agent decisions that already use their reports optimally. In particular, an exhaustive evaluation of 207,632 report designs on small allocation instances recovers the predicted joint optima. Our analysis suggests that reliable agent communication should be evaluated by the complete decisions it supports and the cost of finishing its unresolved cases, instead of solely by report length, local prediction accuracy, or immediate task completion, as is often the case in current practice.

cs.MA · cs.LG · math.OC · stat.ML

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2605.08571

BEACON: Cross-Domain Co-Training of Generative Robot Policies via Best-Effort Adaptation

Antong Zhang, Han Qi, Heng Yang

原文摘要

We introduce BEACON--Best-Effort Adaptation for Cross-Domain Co-Training--a theory-guided framework for training generative robot policies with abundant source demonstrations and limited target demonstrations. BEACON casts cross-domain co-training as a discrepancy-aware importance-reweighting problem, jointly learning a diffusion-based visuomotor policy and per-sample source weights through an objective inspired by the Best-Effort Adaptation (BEA) bound. To make this approach practical for high-dimensional sequence policies, we develop scalable instance-level discrepancy proxies, stochastic alternating updates for policy and weights, and a multi-source extension that balances heterogeneous source domains. Across sim-to-sim, sim-to-real, and multi-source manipulation settings, BEACON improves robustness and data efficiency over target-only, fixed-ratio co-training, and feature-alignment baselines. Without an explicit alignment objective, BEACON also brings source and target representations closer together as an implicit result of discrepancy-aware co-training.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2511.00532

AQBuc: Air Pollution Forecasting in Bucharest

Dragoş-Andrei Şerban, Răzvan-Alexandru Smădu, Dumitru-Clementin Cercel

原文摘要

In recent years, air pollution, especially the particulate matter $2.5\,μ\text{m}$ (PM2.5), has become a growing concern mainly in urban areas. Exposure to air pollution is linked to the development of numerous health conditions, such as the aggravation of respiratory diseases, cardiovascular disorders, impaired lung function, and even cancer or early death. Forecasting future levels of PM2.5 has become increasingly important, as it can provide early warnings and help prevent diseases. We propose AQBuc, a dataset for predicting PM2.5 pollution in Bucharest, Romania, introducing an understudied location, as most work in this field has focused on Asian cities. Our dataset allows us to benchmark models for predicting future levels of PM2.5 across various time horizons. We evaluate a broad range of methods, including machine learning models, such as linear regression and ensemble-based methods, deep learning models based on recurrent networks and transformers, and large language models (LLMs). Specialized time-series models handle temporal data better, especially on longer horizons, while tree ensembles are more cost-effective for short horizons. Meanwhile, LLMs struggle to capture continuous sensor dynamics without specialized architectural changes.

cs.LG · cs.AI · cs.CY · stat.AP

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2505.23565

DRO: A Python Library for Distributionally Robust Optimization in Machine Learning

Tianyu Wang, Jiashuo Liu, Henry Lam, Hongseok Namkoong, Jose Blanchet

原文摘要

We introduce dro, an open-source Python library for distributionally robust optimization (DRO) for regression and classification problems. The library implements 15 DRO formulations and 9 backbone models, enabling 85 distinct DRO methods. Furthermore, dro is compatible with both scikit-learn and PyTorch through a unified interface, and uses approximation techniques to reduce runtime for some computationally demanding formulations. Comprehensive documentation is available at https://python-dro.org.

cs.LG · cs.MS · math.NA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01300

Accelerating posterior sampling in Bayesian inverse problems with diffusion models: an online-offline perspective

Duc-Lam Duong

原文摘要

Bayesian inverse problems require both informative prior models and computationally feasible posterior inference. Score-based diffusion models provide expressive learned priors, but their use for inversion introduces a conditioning problem: the score of a noised posterior is generally unavailable from a prior score alone. This selective survey reviews the principal approaches to that problem, including likelihood guidance, spectral and projection methods, sequential Monte Carlo corrections, and amortized conditional training. We organize methods by how they split cost between offline preparation and online inference, and compare their assumptions, advantages and limitations, with emphasis on computational speedups. Some ongoing directions and open questions are gathered at the end.

cs.LG · math.NA · math.ST · stat.ML

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01299

Best-of-Both-Worlds FTRL for Linear Bandits: The Interplay of Information Acquisition, Corruption, and Geometry

Canzhe Zhao, Vincent Y. F. Tan

原文摘要

We study \textit{best-of-both-worlds} (BOBW) algorithms for linear bandits, which aim to simultaneously guarantee $\gO(\sqrt T)$ regret in the adversarial regime and $\gO(\log T)$ regret in the stochastic regime over $T$ rounds. For finite action sets, we introduce \emph{information-acquisition follow-the-regularized-leader} (\IAFTRL{}), the first BOBW algorithm based purely on the follow-the-regularized-leader (FTRL) framework that attains an $\gO(\cstar\log T)$-type regret in the stochastic regime, where $\cstar$ is the information-acquisition complexity that characterizes instance-dependent optimality in linear bandits, while retaining the $\gO(\sqrt{T})$ regret guarantee in the adversarial regime. This resolves the open problem posed in~\cite{LeeEtAl2021,KongZ023,ItoT23,ZhaoItoLi2025}.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01295

[Re] Pure Noise to the Rescue of Insufficient Data

Seungjae Ryan Lee, Seungmin Brian Lee

原文摘要

We examine the main claims of Zada, Benou, and Irani (2021), which states that in an image classification task with imbalanced training data, (i) using pure noise to augment minority-class images encourages generalization by improving minority-class accuracy. This method is paired with (ii) a new batch normalization layer that normalizes noise images using affine parameters learned from natural images, which improves the model's performance. Moreover, (iii) this improvement is robust to varying levels of data augmentation. Finally, the authors propose that (iv) adding pure noise images can improve classification even on balanced training data. We implemented the training pipeline from the description of the paper using PyTorch and integrated authors' code snippets for sampling pure noise images and batch normalizing noise and natural images separately. We reproduced the main claims that (i) oversampling with pure noise improves generalization by improving the minority-class accuracy, (ii) the proposed batch normalization (BN) method outperforms baselines, (iii) and this improvement is robust across data augmentations. Our results also support that (iv) adding pure noise images can improve classification on balanced training data. However, additional experiments suggest that the performance improvement from OPeN may be more orthogonal to the improvement caused by a bigger network or more complex data augmentation.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01287

Dynamic Click Destinations: Personalizing Post Ad-Click Landing Experiences

Apurva Pathak, Yuya Shiraki, Iman Jenabzadeh, Nikhil Bhale, Yuwen Tan, Shashank Rangarajan, Amirmehdi Sharifzad

原文摘要

Apps that monetize outbound (offsite) ad clicks have multiple post ad-click levers that influence user and business outcomes, including the landing page shown on the advertiser's site, whether the click resolves to a webpage or an app on the user's device, and whether it resolves to a native in-product surface or an external website. We refer to personalization over these levers as post ad-click destination automation. The optimal destination is heterogeneous across users, advertisers, placements, devices, and inferred intent, making static, one-size-fits-all destination policies suboptimal. We present Dynamic Click Destinations (DCD), a production personalization framework that selects a destination for each eligible offsite ad click to maximize Advertiser Value (AV), a value-weighted conversion outcome. DCD applies to any post-click destination decision where the system can choose among destination options at click time and downstream conversion feedback is available. We describe an evolution of learning objectives from post-conversion destination inference, to two-task destination-conditioned conversion prediction, to a single-task policy model that directly predicts which of two destinations is better for a given click, trained with Advantage Actor Critic (A2C)-inspired soft labels derived from a higher-capacity destination-conditioned "critic" model. In a web-vs-app case study, online A/B experiments at scale on a major advertising platform show that per-click personalization with DCD outperforms both static routing policies, with the single-task objective delivering the largest gains. These results highlight post ad-click destination as an important and under-explored lever in conversion optimization and demonstrate that personalized destination automation can outperform one-size-fits-all destination policies.

cs.IR

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.06699

RoboSnap: One-Shot Real-to-Sim Scene Generation for Generalizable Robot Learning and Evaluation

Shujie Zhang, Jingkun Yi, Weipeng Zhong, Zirui Zhou, Yangkun Zhu, Hanqing Wang, Xudong Xu, Weinan Zhang 等 9 位作者

原文摘要

Recovering real-world scenes as interactive simulation environments can enable generalizable robot learning and reproducible policy evaluation. However, constructing scenes that are both physically stable and visually faithful remains slow and expensive. In this work, we present RoboSnap, a real-to-sim framework that turns a single RGB image into a simulation-ready scene. The key idea is a layered design that separates the physics-critical interaction area from the surrounding visual context: collision-aware foreground assets are refined for stable robot interaction, while a 3D Gaussian splatting visual layer preserves faithful background appearance under novel views. Experiments on DROID scenes and real-robot tasks show that RoboSnap achieves reliable trajectory replay in the recovered scenes, supports task-specific synthetic data generation for policy training, and yields meaningful sim-real correlation for policy evaluation. To further support real-to-sim research, we introduce DROID-Sim, a real-to-sim companion dataset constructed from 564 real-world scenes in DROID. Extensive experiments suggest that the value of real-to-sim methods lies not only in high-fidelity visual reconstruction, but in turning real environments into reusable infrastructure for robot learning and evaluation.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01284

Character-Name Concentration in LLM Fiction: Measuring Displacement and Mitigating Repetition

Alejandro Mozo Quesada, Sergio Gálvez Rojas, Francisco López Valverde

原文摘要

Character names in large language model (LLM) fiction concentrate on a handful of recurrent names such as 'Elara' and 'Elias', a measurable face of AI slop. Existing fixes either require logit access or retraining, or operate per request and cannot control concentration across a corpus. How much prompt-level strategies reduce concentration, and at what cost to name naturalness, has not been systematically measured. We present an empirical study of name-concentration control across eight strategies and introduce speculative naming, a fine-tuning-free method that adapts the draft-verify-reject paradigm of speculative decoding from tokens to names. The method requests candidate name-sets through verbalized sampling, scores them against a SlopTracker that combines a per-model fingerprint with a runtime counter, and selects through a temperature-controlled rule or its strict variant (HARD). We use 20 LLMs to generate 44,782 stories in four languages from text and visual stimuli (88,919 named characters), and measure concentration with a list-free collision metric (Slop Ratio, SR) decomposed into known and emergent components. List-based strategies do not remove slop: static banlists displace concentration to emergent names and more than halve the share of attested real names. Speculative naming removes it: the sampling scaffold alone cuts SR by 89% on average, the dynamic tracker brings the total reduction to 95%, mean collision probability falls from 7.3% to 0.4%, and known-name collisions hold at zero while naturalness stays close to baseline (42-69% attested by language) and within-story collisions drop from 4.3% to 0.2%. The strategy ranking is stable across languages, model families, and name-counting conventions. Name concentration is thus controllable at the corpus level without logit access or retraining; broader creative diversity beyond naming remains open.

cs.CL · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.01283

DriftingPlanner: Drifting Generative Models for One-Step Trajectory Planning in End-to-End Autonomous Driving

Ziming Liu, Yizhan Qu, Leichen Wang, Hao Sun

原文摘要

End-to-end planners decode the ego trajectory either by coordinate regression, which is fast but blends plausible maneuvers into one trajectory that may follow none of them, or by diffusion and flow matching, which model the trajectory distribution but need several inference steps. Drifting models move this iteration into training: a one-step generator learns to follow an attraction--repulsion field between generated and ground-truth samples. Drifting was developed for class-conditional image generation, where every class supplies many positive samples. In planning, every scene is its own condition. Pooling scenes would attract the generated trajectories of one scene to the ground truth of unrelated scenes, whereas computing the field per scene leaves a single positive, the expert trajectory. Mutual repulsion then holds the generated trajectories on a shell around it, so any single generated trajectory deviates from the ground truth. We present DriftingPlanner, a trajectory planner trained with the drifting loss alone, without any coordinate regression term. It computes the field per scene and removes the shell at inference: it decodes every noise vector together with its negation in one batched pass, and averaging each antithetic pair cancels the linear part of the displacement from the shell center. On NAVSIM navtest, DriftingPlanner reaches a state-of-the-art 93.75 PDMS, and with single-step inference its drifting decoder comes within 0.13 PDMS of the best diffusion or flow matching setting, which needs ten inference steps.

cs.CV · cs.RO

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arXiv ↗PDF ↗