Noticias de IA - ítems

arXiv:2609.09226v2 Announce Type: replace Abstract: Understanding the brain's mechanisms is crucial in neuroscience, particularly for simulating natural intelligence and advancing artificial general intelligence (AGI). Liu et al. (2024) and Deco et al. (2025) propose that quantum entanglement may underlie the synchronization of millions of neurons, introducing the Liu-Chen-Ao (LCA) hypothesis of entangled nerve fibers. However, directly testing this idea remains challenging. Here we employ a nonlocal many-body wave equation, introducing a generalized probability-wave framework to model collective behaviors among adaptive, interacting agents, particles, or neurons. This approach enables testable eigenmodes, offering an indirect means of examining the LCA hypothesis by observing patterns in collective trading behaviors. These patterns, rooted in behavioral psychology, are thought to mirror the internal brain's decision-making and underlying mechanisms. Our analysis of Chinese intraday stock market data shows that adaptive entangled game modes explain 82%-94% of decision patterns, sharply contrasting neoclassical finance predictions assuming independent rational agents. Additionally, 2-12% of actions adapt to news, events, and environmental changes, marked by rapid equilibrium shifts, while truly independent behaviors make up less than 5%. These results empirically support the LCA hypothesis and underscore the need to simulate the brain's mechanisms and generate adaptive, entangled modules for AGI. They also expose the shortcomings of conventional artificial neural networks (ANNs) that depend on vast, opaque parameters. By combining ANN-based AI with probability-wave-based entangled brain simulations, machine learning enables the creation of human-like processing units (HPUs). Such HPUs promise more efficient and robust AGI, particularly for embodied intelligence and robotics.

arXiv:2609.07299v2 Announce Type: replace Abstract: Learned world models typically assume that observations arrive synchronously, an abstraction inherited from simulators that return a complete state vector at each environment step. Physical sensing instead operates at heterogeneous rates, leaving most observation channels stale at any given instant. Interpolating stale channels introduces measurements that were never observed, while downsampling to the slowest sensor discards valid measurements. A natural alternative is to zero-order-hold the most recent reading and provide the known sampling schedule to the model through two features, staleness and time-to-refresh. We test this prediction using transformer world models across three regimes of increasing causal coupling: open-loop rollouts in continuous-control locomotion, closed-loop model-predictive planning in which each learned model serves as the planner dynamics, and a linear latched-actuator system in which refresh events apply a zero-order-held command to the plant. Our findings show that the effectiveness of time-to-refresh depends on the causal role of the sampling schedule, specifically when refresh events affect the system rather than merely report its state. These results establish when sampling schedules provide useful information for predictive world models operating under asynchronous physical observations.

arXiv:2609.03966v3 Announce Type: replace Abstract: Tool-use evaluations typically measure calls through a serving stack, which can report zero calls even when model outputs contain call-shaped payloads. We study interface-induced trajectory censoring: a mismatch between model emissions and the interface that converts them into actions. On selected BFCL v4 subsets, changing only the serving adapter while holding model weights and other evaluation settings fixed moves scores under the official scorer from 0.00 to 0.96 on simple_python and from 0.00 to 0.19 on multi_turn_base. A template-parser factorial experiment shows strong complementarity: neither component replacement alone restores parsing from the baseline, whereas their joint replacement recovers 196/200 first-request parses. Across five Qwen2.5-Coder checkpoints, classifier-positive emissions increase with model size while the mismatched interface reports no calls; a separate matched-interface Qwen3 ladder exhibits few silent emissions through 8B. Experiments on 115 tau-bench retail tasks extend the mechanism to a stateful, non-code environment. In a preregistered, single-seed, 150-step 7B LoRA experiment, changing only the parser registered in verl increases tool executions from 10 to 16,844. However, neither a shared programmatic-feedback evaluation nor a post-hoc common native function-calling evaluation detects a held-out success gain from repair; rescue counts do not increase. Interface repair thus restores tool-mediated experience without a detected performance gain in this setting. These findings show that tool-use measurements depend on the model-interface configuration and motivate layer-wise observability and conformance checks before attributing evaluation or training outcomes to the model alone.

arXiv:2608.26950v2 Announce Type: replace Abstract: Large Language Models (LLMs) are evolving from performing end-to-end mathematical reasoning to integrating agentic intelligence. However, most existing math benchmarks evaluate only final answers. This outcome-oriented evaluation provides limited diagnostic value for identifying process-level failures or rigorous logic, failing to guide the transformation of LLMs into robust agents. To bridge this gap, we present a process-level benchmark designed to evaluate the inherent agentic mathematical reasoning abilities of LLMs. Our framework aligns problem-solving agentic behaviors with a structured taxonomy of reusable mathematical atomic capabilities. We design a comprehensive suite of planning, action, and feedback tasks across both textual and multimodal contexts, supported by an automated pipeline that synthesizes high-quality trajectories and produces fine-grained annotations via controlled LLM rewriting. Experiments reveal that models with similar end-to-end accuracy can exhibit markedly different agentic capability profiles. This demonstrates that process-level evaluation is crucial for interpreting the true potential of LLMs and guiding the development of next-generation mathematical agents.

arXiv:2608.22887v2 Announce Type: replace Abstract: Large language models are increasingly used to judge people, and their answers can depend on inputs correlated with a protected attribute such as race, even when that attribute is never shown. Interpreting this reliance requires knowing how much predictive information those inputs add, but audits often report only whether a decision changes. We introduce an evidence-referenced audit that compares proxy reliance with two references, the known outcome rule and a Bayesian linear regression fitted to the same labeled examples, while measuring ranking accuracy separately. We apply it to four models in a simulated patient-ranking task with known field weights. We find four results. First, at zero predictive value, the models still relied on fields that carried no information, while the same-example learner did not. Second, as proxies became predictive, reliance grew far less than the reference. Third, display format mattered. Ordered labels drew more reliance than arbitrary labels, a direction that held in all four models. Fourth, ranking accuracy did not consistently track these changes in reliance. Based on these findings, we argue that decision changes and accuracy cannot show whether proxy reliance is warranted by the evidence. Audits should report reliance against an explicit predictive reference, alongside accuracy. Ordered fields whose values move with group membership, such as scores or income bands, are a useful starting point. Together, these results make it possible to measure how far reliance departs from the evidence, rather than infer it from observed choices alone.

arXiv:2608.14590v3 Announce Type: replace Abstract: Large Language Model (LLM) agents take irreversible real-world actions, yet no system gives formally grounded, task-level safety guarantees for their plans. In a PRISMA 2020 systematic review, we searched six databases (20-29 May 2026) for work on LLM agent specification, verification, and enforcement published in 2022-2026. We narratively synthesized 38 studies, assessed risk of bias per study, and graded evidence certainty with GRADE. Three authors independently screened a repeated search to validate the single-reviewer screening (agreement: Fleiss' $\kappa = 0.81$). First, specification is the bottleneck: on one benchmark, only 24.8-34.2% of LLM translations from natural language into Planning Domain Definition Language (PDDL) are semantically correct. Second, runtime monitoring is the most mature enforcement strategy; it reduced unsafe actions by 65.37% in one household-agent study, but its guarantees cover only single steps or short horizons. Third, in one $\tau$-Bench study of transactional tool-use agents, blocking 94% of unsafe actions still left fewer than 5% of tasks completed both safely and correctly in most settings, because agents found other unsafe routes (the verifier tax). Fourth, no approach simultaneously achieves soundness, completeness, scalability, semantic grounding, and utility preservation. For seven of the nine principal claims, GRADE gives the lowest certainty rating (Very Low), meaning further studies are likely to produce substantially different figures; each rests on one or two studies in narrow domains. Two of the open problems we rank highest are translating requirements into formal specifications that keep their meaning and enforcing safety over whole plans rather than single actions. We contribute a three-level taxonomy and an agenda of ten open problems.

arXiv:2608.12002v2 Announce Type: replace Abstract: While autonomous agents show promise for network operations, existing telecom troubleshooting evaluations fail to capture the partial observability and heterogeneity of real-world telecom environments. We introduce CTBench, a public benchmark evaluating agent troubleshooting capabilities in root cause analysis (RCA) and path restoration. Constructed by domain experts, CTBench features rich metadata and golden evidence trajectories, utilizing metrics that assess both final accuracy and diagnostic reasoning. Our evaluation of state-of-the-art agents reveals that while they excel at path endpoint identification, they severely underperform in RCA. Crucially, even when agents produce correct answers, they frequently fail to gather the requisite diagnostic evidence demanded in operational practice. Finally, we demonstrate that increased computational expenditure does not guarantee improved diagnostic competence, highlighting critical gaps in current agent capabilities.

arXiv:2607.22602v2 Announce Type: replace Abstract: Test-time scaling improves large language model reasoning, but spends compute uniformly along a reasoning trace, even in stretches that are already stable. Motivated by the observation that reasoning failures show an early onset of uncertainty, we introduce DeepLook, a training-free decoding framework that monitors segment-level confidence, branches only at uncertainty bottlenecks, and ranks candidate continuations by Average Lookahead Confidence (ALC)---their mean confidence over fixed-horizon rollouts---before completing and voting over a pruned subset. On four competition mathematics benchmarks across four open models, DeepLook matches or exceeds the accuracy of DeepConf-low in 12 of 16 settings while generating 87.3% fewer tokens, showing that a model's own confidence is informative enough to decide where extra decoding compute is worth spending. On knowledge-intensive GPQA-Diamond the accuracy advantage disappears even though the token saving does not: coherent hallucinations also receive high ALC, so internal confidence localizes instability, not falsehood. Code is available at https://github.com/txyang1/deeplook.

arXiv:2607.20491v4 Announce Type: replace Abstract: When a financial agent repeats its decision, how much does that tell us about the actions behind it? DFAH-Bench examines this question through replay experiments, a controlled test of omitted tool calls, and banking tasks with authorization checks. Across 570 eligible prospective replay episodes, decisions agreed at rates of 94-95%, while tool paths, including inputs and results, agreed at 45-52%; one condition fell one group short of the planned minimum. In a separate 48-execution diagnostic, deleting the same calls from repeated records left agreement perfect. In banking probes, repeated authorization decisions sometimes conflicted with constructed rule labels. The $\tau$-Knowledge banking study scheduled 1,080 episodes, with 1,033 known outcomes. Structural checks alone completed more primary tasks than either model-based gate-and-recovery bundle. Input limits and recovery behavior materially shape these results. The typed-choice bundle had lower mean episode cost than the generative bundle on complete task pairs, but fewer successes across the primary schedule under every assignment of unknown outcomes. Missing outcomes prevented the planned confirmatory tests, so the banking comparisons are descriptive. Together, the findings show that agreement alone cannot establish reliability: it can conceal changes in execution, systematic omissions, and unsupported decisions. Evaluation therefore needs to connect what the agent did, the evidence and rules governing its action, and whether it completed the task.

arXiv:2606.20208v2 Announce Type: replace Abstract: Machine learning models are predominantly evaluated through predictive performance metrics such as ranking quality, prediction error, or classification accuracy. While these metrics effectively quantify how closely predictions match the ground truth, they do not assess whether model outputs respect predefined logical or domain-specific constraints. In high-stakes applications, including healthcare, finance, and autonomous systems, logical consistency can be as critical as predictive accuracy, yet no standard metric captures this dimension. We introduce the Rule Violation Score (RVS), a complementary evaluation metric that quantifies the extent to which a predictive model respects a given set of logical rules, independently of predictive accuracy. RVS treats hard rules (strict constraints) and soft rules (statistical regularities) differently, can be evaluated on any dataset and on any predictive model expressed over a relational vocabulary, and can be computed using SQL queries that are automatically generated for Horn rules. Beyond evaluating models, RVS can also evaluate the logical consistency of training datasets and help identify poorly defined rules. We evaluate RVS on three benchmarks covering knowledge graph link prediction and relational regression, including rule-based, embedding-based, and neuro-symbolic predictive models. Our results demonstrate that two models achieving comparable predictive accuracy can exhibit substantially different levels of logical compliance, revealing differences in model behavior that standard metrics fail to capture.