Noticias de IA - ítems

arXiv:2610.34329v1 Announce Type: cross Abstract: Large language models (LLMs) can act as decision models that return a probability distribution over a fixed set of options, but deployed decision models face domain, label-prior, emerging-class and candidate-set shift. We study how such models fail under shift and propose SHIFT-Jev, a monitoring, abstention and continual-adaptation procedure for them. It scores the risk of each decision from uncertainty, format and option-consistency signals, certifies abstention thresholds with Learn-then-Test, monitors the decision stream with drift and label-shift detectors, and accepts a model update only when an expected-gain gate finds a significant held-out improvement without loss on earlier regimes. On a benchmark of ten shifted test environments with Qwen3-0.6B and Qwen3-4B (three seeds each) and Phi-4-mini, fine-tuning with KL anchors and temperature scaling lowers calibration error under shift from 0.289 to 0.087. At a target risk of 10%, a learned uncertainty score with a certified threshold exceeds the target in 3 to 5 of 27 environment-seed tests, against 5 to 15 for a max-probability score with the same certification and 9 to 14 for conformal prediction sets; drift-based routing changes little. A chi-squared test on predicted labels detects 95 to 99% of class-prior shifts that confidence- and embedding-based detectors miss. In continual adaptation, SHIFT-Jev reaches 70.5% accuracy on Qwen3-0.6B, above replay, LwF, EWC and fine-tuning baselines in every seed, whereas on Qwen3-4B its conservative gate leaves it 0.4 to 1.4 points behind them.

arXiv:2610.34317v1 Announce Type: cross Abstract: With the rise of Large Language Models (LLMs), software developers are presented with a powerful new approach: delegating software development tasks to LLMs. Existing studies have explored task delegation for a subset of tasks and factors. Critically, what tasks developers delegate to LLMs, to what extent, and why they delegate them, is not yet well understood. We explore these questions through 22 interviews and a survey with 160 software practitioners. Developers reported employing four modes of task delegation to LLMs: full delegation, partial delegation, self-delegation, and task avoidance. Drawing on our qualitative findings, we discuss the rationales behind these choices and explore how task difficulty, task risk, developer motivation, and trust appear to be associated with software development task delegation to LLMs. Our statistical analysis reveals that trust is a significant factor associated with task delegation to LLMs. Finally, we share actionable implications for human-AI collaboration in software research, and for software practitioners to make the most of their newly found powers of delegation.

arXiv:2610.34297v1 Announce Type: cross Abstract: The Babylonian Astronomical Diaries record the sky over nearly six centuries. Many tablets are broken and have lost their date formula. Experts dated many of them one tablet at a time in the standard edition, Astronomical Diaries and Related Texts from Babylonia (ADART). They used the recorded sky and other evidence, such as year formulas and royal names. Our system automates the astronomical part for all tablets with the same rules. A rule-based parser turns the recorded observations into clauses, short statements with fixed fields. A language model checks them against the text and adds observations that the parser missed. Astronomical tools test the clauses in all 701 years from 626 BCE to AD 75. The system ranks the years by how well they agree with the clauses. The system withholds published dates until its results are final, and the output lists the passages and computed positions behind each candidate year. On 120 dated tablets, the years tied at the top of the ranking include the published year in 96% of cases. For 86 of them, this top group is a single year, the published year in 84. In 119 of the 120, the reported candidate set contains it. For 56 undated entries of ADART IV, the system gives ranked candidate years. For each tablet, the results show how far astronomy alone narrows the year. An interactive page at https://tingyuansen.github.io/Babylonian_Dating/ follows each tablet and links the code, protocol and results.

arXiv:2610.34290v1 Announce Type: cross Abstract: Broken access control (BAC) ranks among the most severe web-application vulnerability classes, yet its automated repair remains largely unexplored. Unlike memory-safety vulnerabilities, BAC is a logic vulnerability whose repair demands a deep understanding of an application's access-control model and business semantics, which traditional automated vulnerability repair techniques handle poorly. Large language models (LLMs) open a promising avenue for this problem, but no standard benchmark exists for studying how well they repair BAC vulnerabilities. We construct BAC-Bench, the first benchmark for BAC repair, consisting of 46 real-world BAC vulnerabilities drawn from six widely used web applications spanning four programming languages. Each vulnerability is equipped with a sandboxed runtime environment, a PoC validation program and a functional test program. We further propose a BAC repair-strategy framework that characterizes any BAC patch along four dimensions, Position, Coverage, Mechanism, and Content. Evaluating eight base LLMs and two commercial LLM agents on BAC-Bench, we find that single-turn base LLMs repair 37.68% of the vulnerabilities on average, while two minimal scaffolds, Apply-Feedback and Navigate-and-Locate, raise this average to 66.03%, with the best base LLM reaching 75.36%, close to the 78.99% of the best agent. Notably, some LLM-generated patches surpass the official patches, and one incidentally closed a zero-day vulnerability that had lain dormant for three years. Our findings delineate the current capability boundary of LLM-based BAC repair and offer guidance toward its practical adoption.

arXiv:2610.34272v1 Announce Type: cross Abstract: Multi-task post-training can develop domain specialists through independent reinforcement learning (RL) and consolidate their capabilities through multi-teacher on-policy distillation (MOPD). However, standard MOPD generates on-policy student responses and keeps multiple teachers online throughout consolidation, introducing substantial system costs. We view this pipeline as domain-wise exploration followed by cross-domain exploitation. Domain RL produces not only the final specialists, but also the trajectories collected throughout their training. Existing methods primarily use the former while leaving the latter underutilized. We introduce DoubleFree, which reuses these historical trajectories for rollout-free consolidation and precomputes teacher supervision for online-teacher-free student training. To understand why this works, we analyze when updates from historical trajectories can approximate on-policy distillation updates and how combining trajectories across exploration stages can better support an evolving student. DoubleFree requires only minimal changes to existing post-training frameworks. Across math, code, and instruction following on Qwen3-4B, Qwen3-8B, and SmolLM3-3B, DoubleFree achieves $2.99$-$5.75\times$ consolidation speedups and reduces GPU-hours by $80.9$-$90.1\%$, while maintaining comparable multi-domain performance.

arXiv:2610.34258v1 Announce Type: cross Abstract: Enterprise sales-order systems rely on manually maintained configuration rules to determine fields such as payment terms, shipping points, and sales ownership. When these rules are incomplete or stale, operators must enter values by hand, and downstream errors spread into billing, logistics, and reporting. This paper studies whether missing or outdated rules can be recovered from historical transactions instead of being relearned as opaque statistical patterns. We introduce a knowledge-graph-guided workflow in which an LLM agent proposes candidate business keys, meaning column combinations such as customer and document type, validates them through SQL profiling, and compiles the resulting evidence into deterministic cascade lookups. The LLM is used only during offline rule discovery. At inference time, prediction is a transparent key lookup with no model-serving requirement. Experiments on enterprise sales-order prediction and public relational benchmarks show that cascades are most effective when fields follow stable business rules, while learned graph models remain preferable when prediction depends on temporal or neighborhood patterns. Drift analysis further turns residual errors into diagnostics for configuration maintenance. Code and data: https://github.com/Fireblossom/CascadER.

arXiv:2610.34253v1 Announce Type: cross Abstract: AI coding agents routinely clone repositories, install packages, and read source code obtained from the Web, so publicly hosted code becomes part of the context that shapes the code they write. We show that this Web-borne context can be weaponized without any explicit instruction. We present NameTrap, a source-code poisoning attack that only renames identifiers in third-party Python files to steer AI coding agents toward attacker-targeted Common Weakness Enumeration (CWE) vulnerabilities. An attacker-side coding agent iteratively aligns identifiers with a CWE-specific naming convention and confirms through local probe completions that the renamed file elicits the target weakness, while the released bait contains neither instructions nor vulnerable statements. We evaluate NameTrap across six AI coding agents, ten underlying language models, and four vulnerability categories: CWE-78, CWE-79, CWE-327, and CWE-502. Starting from files whose clean versions never yield the targeted CWE, identifier-level manipulation achieves overall attack success rates of 22.5\% to 56.0\% across the six agents, reaching 75.0\% for certain agent--model configurations, and substantially outperforms random renaming, neutral renaming, and the prior identifier-poisoning attack XOXO. The attack remains effective under Traditional Chinese prompts and transfers across models, while a security-reminder defense fails to consistently prevent vulnerability introduction. Moreover, three AI-based security analyzers and four static analyzers report none of the targeted weaknesses in the poisoned files. These findings expose an overlooked attack surface of agents that consume Web content: seemingly harmless variable names in untrusted, Web-hosted code can compromise the security of downstream software.

arXiv:2610.34238v1 Announce Type: cross Abstract: In this paper, we introduce $\Delta\tau$-Bench, an executable agent benchmark that models the structural evolution of large tool ecosystems. In $\Delta\tau$-Bench, independently evolving services continually reshape how tasks can be accomplished: familiar tools may disappear or be replaced, while more efficient alternatives emerge amid overlapping functionality and noisy documentation. Against this moving capability frontier, we evaluate whether agents can proactively discover efficient tool-use paths and promptly revise their accumulated procedural knowledge, bringing together demands that existing benchmarks fail to examine. $\Delta\tau$-Bench comprises 1,588 validated multi-turn tasks across 42 domains and six successive ecosystem states generated through capability-preserving transformations. Our evaluation shows that even the strongest evaluated model or harness achieves only 42.3% task success, while initial gains from existing skill-learning methods can not consistently translate into better adaptation as the ecosystem evolves. These findings motivate Steward, an ecosystem manager that jointly manages tools and procedural knowledge through incremental skill maintenance and dynamic provisioning during task execution. Experiments show that Steward sustains higher task success across ecosystem changes with lower skill-maintenance overhead, while improving both recovery from disrupted workflows and adoption of more efficient alternatives.

arXiv:2610.34214v1 Announce Type: cross Abstract: Cross-modality medical image translation is challenged by anatomically inconsistent synthesis that can compromise clinical diagnosis. We propose StaX-Net, a Stackelberg interleaved asymmetric network that incorporates a partially coupled encoder-discriminator structure, and a 2D feature aggregation and spatial transformation (FAST) layer that distills target-supervised priors under the learning using privileged information paradigm. The shared blocks between the generator and discriminator enhance architectural feature alignment. To stabilize the leader-follower training, a Stackelberg-inspired asymmetric optimization scheme employs explicit shared-parameter freezing and asymmetric learning rates. The FAST layer distills target-domain priors during training and applies a source-only transform at inference. This constrains the synthesis process through target-informed feature representations relative to standard generative architectures. The code is available on GitHub for public use.

arXiv:2610.34208v1 Announce Type: cross Abstract: Offline safe reinforcement learning (RL) learns safety-aware policies from static datasets while satisfying safety constraints during deployment. Motivated by the strong trajectorymodeling capabilities of Transformers, recent Decision Transformer (DT)-based methods formulate offline safe RL as conditional sequence modeling by conditioning action generation on return-to-go (RTG) and cost-to-go (CTG) tokens, thereby enabling adaptation to varying safety budgets. However, existing DT-based methods are developed for single-constraint tasks. For tasks with multiple constraints, generalizing learned policies to unseen combinations of safety budgets remains challenging due to the conflicting requirements imposed by heterogeneous constraints. Moreover, the quadratic complexity of Transformer architectures significantly increases the computational overhead in multi-constraint tasks, as representing additional constraints requires more CTG tokens. Recently, Mamba has emerged as an efficient sequence-modeling backbone, offering linear computational complexity and strong generalization capabilities. In this paper, we propose CORMA, a constraint-routed decision Mamba framework for generalizable multi-constraint offline safe RL. CORMA decomposes policy inference into constraint-aware RTG estimation and adaptive action generation, where constraint-specific experts model feasible RTG distributions and a router-guided product-of-experts mechanism fuses these distributions to provide safety-aware guidance for a Mamba-based action decoder. A three-stage optimization strategy is designed to improve training stability. Extensive experiments on OSRL benchmarks demonstrate that CORMA achieves superior reward-safety trade-offs, reduces inference latency by 37% compared with DT-based methods, and exhibits stronger zero-shot generalization to unseen safety-budget combinations than offline safe RL baselines.