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Papers y preprints de los feeds de investigación que seguimos.

6415documentos

arXiv AI Papershace 23 días

Every Alarm Has a Cause: An Open Generator of Telecom Alarm Storms with Exact Root Causes, Bound to NORIA-O, RFC 8632 and TMF642

Alarm root-cause analysis is studied on data that cannot be shared. Operator logs are confidential, so what the field publishes is a causal graph between alarm types rather than per-incident causes, a fixed scenario set, gated, or never released. Two recent benchmarks carry per-incident root causes at scale; neither states a download location or a licence. We give a generator rather than a dataset. It runs on real backbone topologies from the Internet Topology Zoo and records the exact root cause and the propagation path of every alarm -- a by-product of generation, not a labelling pass. Every emitted field binds to a term in NORIA-O, RFC 8632, RFC 8345 or TMF642, and an unknown term fails the run. We ship fixed configurations, splits, a scorer and nine baselines. A one-line rule -- rank the device that raised the earliest critical alarm -- takes top-1 at 0.650 against 0.575 for our own temporal method, and wins 13 of 15 runs over three backbones and five seeds. An LLM given the same view reaches 0.600, the same model allowed to query the graph 0.625, and the rule wins on real operator alarms too, at 0.773: a property of alarm streams, not of our generator. One fault class separates the table: when a site loses power its PoP reports nothing, so every method that ranks alarmed devices scores 0.000 or near it, while the two that walk the topology to a silent candidate take 1.000 and 0.833.

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arXiv AI Papershace 23 días

EPIC: Rethinking Trust Regions for Autoregressive LLM Policies via Exact-KL Prefix Importance-Ratio Clipping

Reinforcement learning (RL) based on PPO-style clipping has become an effective approach to improving the reasoning capabilities of large language models (LLMs). However, standard token-level clipping measures policy change only through the conditional probability of the current token, while the prefix conditioning context is itself generated by the policy. This mismatch becomes especially consequential in long-response and off-policy training. We reformulate autoregressive generation as a finite-horizon Markov decision process and derive its performance-difference identity under behavior-policy sampling. The resulting change of measure identifies the cumulative likelihood ratio of the generated prefix as the exact importance weight for each stepwise policy-improvement term. Starting from the monotonic-improvement principle of trust-region policy optimization, we then construct a prefix-level trust region using the exact reverse-KL coordinate r-1-r, whose behavior-policy expectation equals the corresponding prefix-distribution KL divergence. This yields EPIC, a PPO-style sequence-clipping method that directly regulates prefix-level policy drift without relying on a local quadratic approximation or expert-routing replay. Experiments on off-policy reinforcement learning with a sparse mixture-of-experts language model show that EPIC consistently outperforms standard and sequence-level clipping baselines across mathematical-reasoning benchmarks, while remaining competitive with routing-replay-based alternatives. Code is available at https://github.com/LiChengxi666/EPIC.

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arXiv AI Papershace 23 días

HiddenLens: A Multi-Layer LLM Framework for Detecting and Resolving Hidden Assumptions in Software Requirements

Natural-language software requirements often omit assumptions about performance, operating conditions, user behaviour, and domain constraints, affecting architecture, testing, and deployment. If undiscovered until implementation, resolving them is far more costly than addressing them early. Although ambiguity and vagueness are widely studied, less attention has been given to assumptions embedded in requirements that appear clear and reasonable. We propose HiddenLens, a novel six-layer BERT-LLM pipeline and, to our knowledge, the first framework engineered to detect, classify, question, and resolve hidden assumptions rather than merely flag ambiguity. Layer 1 uses BERT attention entropy to localise unresolved spans; Layer 2 uses CoT-prompted LLM reasoning to articulate each assumption and its failure risk; Layer 3 uses fine-tuned BERT to classify assumptions into six non-functional requirement categories: performance, security, scalability, usability, reliability, and domain; Layer 4 generates ranked clarification options using FS LLM prompting; Layer 5 enables human validation through confirmation, editing, or rephrasing; and Layer 6 rewrites the requirement using the confirmed resolution, producing an explicit, testable, and traceable requirement. We also introduce the HiddenLens Corpus (HLC), comprising 312 requirements annotated with 847 hidden assumptions across six NFR categories. Inspired by PROMISE and PURE, HLC addresses the lack of annotated data for this task and is, to our knowledge, the first dataset developed specifically for it. HiddenLens outperforms keyword-based and QuARS-Adapted baselines by 71% and 61%, respectively, achieving an end-to-end F1-score of 0.79; its BERT-based NFR classifier achieves an F1-score of 0.847. These results indicate that HiddenLens can help analysts resolve hidden assumptions before they propagate through software development.

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arXiv AI Papershace 23 días

Remembering Through Change: Provenance-Preserving Hypergraph Memory for Multimodal Agents

Long-term memory for multimodal agents remains challenging, as it must preserve fine-grained visual evidence while tracking entity evolution across sessions. Existing abstraction-based memories compress images into text, often discarding visually discriminative details, whereas pixel-preserving memories retain raw observations but struggle to reliably associate diverse observations with persistent entity identities and track their evolving states. To address these limitations, we propose MHG-Mem, a multimodal hypergraph memory that jointly organizes entity identity, state evolution, and provenance to visual evidence. Specifically, MHG-Mem represents distinct entity mentions and their observed facts as individual nodes, grouping identity-consistent mentions and facts through occurrence hyperedges. It then synthesizes aspect-specific evolution states from these grouped observations and links them to supporting entities, facts, and source evidence through evolutionary and evidential hyperedges. To navigate this structured memory, we introduce hypergraph-based agentic search, which adaptively selects an entry layer, traverses typed hyperedges across memory levels, and inspects source observations when fine-grained verification is required. These designs support both detailed visual verification and reasoning over cross-session state changes. Extensive experiments across three benchmarks demonstrate the effectiveness of MHG-Mem, improving performance by 8.20-9.98% over the strongest baseline.

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arXiv AI Papershace 23 días

SCDM: Scalable Causal Discovery in Nonlinear Temporal Systems with Meta-Learning

Causal discovery from nonlinear multivariate time series is challenging in high-dimensional systems, where the number of candidate directed relations grows quadratically with the number of variables. Existing methods are often limited by target-wise model fitting, repeated conditional testing, or expensive graph-search procedures. We propose SCDM, a shared-parameter meta-learning framework for scalable temporal causal discovery. SCDM treats each target variable as a task while learning a shared temporal predictor and a shared causal-strength matrix across tasks. Each meta-episode updates only a subset of target-variable tasks, allowing the model to accumulate structural evidence across the full system without exhaustive target-wise optimization. After training, a post-training graph readout converts the learned predictor into continuous directed causal scores for threshold-independent evaluation. We also provide a finite-error recovery principle showing that, under fully observed delayed-system assumptions and a positive risk-gap condition, thresholding SCDM scores recovers the graph when statistical, meta-optimization, and readout errors are below the population margin. Experiments on controlled synthetic, neuroimaging-inspired, high-dimensional, and real-world temporal benchmarks demonstrate competitive causal recovery and improved scalability in large temporal systems.

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arXiv AI Papershace 23 días

TIDE: Tree-Induced Fine-Grained Diagnostic Evaluation from Model Behaviors

Benchmark performance is the dominant standard for comparing large language models (LLMs), typically summarized by a single aggregate score, such as accuracy, pass rate, or rubric score. This aggregate score provides limited information about a model's underlying capabilities, yet understanding these capabilities is crucial for model development and evaluation. To this end, we propose Tree-Induced Diagnostic Evaluation (TIDE), which recasts diagnostic evaluation as behavior-driven decision-tree induction to perform fine-grained capability diagnosis. TIDE recursively builds a hierarchical capability tree from benchmark samples and model scores: at each node, an LLM judge proposes candidate splits and performs sample-to-group classification, and behavior-based information gain selects among them. We apply TIDE to five models across four benchmarks to demonstrate its effectiveness, and find fine-grained capability contrasts invisible at the aggregate level: Claude Opus 4.6 achieves 100% on microeconomic market reasoning where GPT-5.4 scores 83.3%, but trails GPT-5.4 on contract law reasoning with 77.8% versus 88.9%. We hope TIDE can be extended to past and future benchmarks so that the rich behavioral signal in evaluation results is fully leveraged for understanding model capabilities.

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arXiv AI Papershace 23 días

The Price of Current-Effect Inference in Nonstationary Multi-Armed Bandits

Adaptive experiments use bandit algorithms to favor interventions with higher expected rewards. However, decision-makers also require reliable inference on current treatment effects: differences between intervention and reference-arm mean rewards at the present time, rather than historical averages. These objectives conflict: reward maximization favors better interventions, whereas current-effect inference requires continued exploration of potentially suboptimal interventions as outcomes change over time. We characterize this tradeoff for K-arm bandits with Hölder-smooth mean rewards of exponent β(0,1] and constant L. Requiring high-probability simultaneous coverage of all current effects by confidence intervals of width at most w, we establish matching minimax bounds, up to logarithmic factors, on expected dynamic regret. Over horizon T, our algorithm TRACE (Tracking Rewards and Current Effects) achieves regret of order ${O}_β({KT}+K^{β/(2β+1)}L^{1/(2β+1)}T^{(β+1)/(2β+1)}+Kw^{-2}(1+L^{1/β}w^{-1/β})), where the notation hides logarithms and constants depending only on β$. This regret--precision tradeoff separates reward-learning regret from the cost of refreshing current-effect estimates, which remains unavoidable even when one arm is uniformly optimal. An empirical-Bernstein e-process extension adapts exploration to realized prediction errors while preserving worst-case guarantees. When within-block drift is negligible relative to sampling error, all observations, including reward-learning observations, can be reused for asymptotically normal pointwise inference and stronger local stationarity yields local asymptotic efficiency. Finally, we show that regret constraints can prevent honest adaptation to unknown smoothness and support our theory with extensive numerical experiments.

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arXiv AI Papershace 23 días

FaultGene: LLM-Assisted Fault-Augmented Test Generation for Hardware-in-the-Loop Validation of Autonomous Driving Systems

Reliable validation of autonomous driving cyber-physical systems requires testing not only nominal driving behavior but also failures arising from faults in sensing and embedded computation. Physical road testing provides realistic system behavior but is costly and difficult to reproduce, while simulation enables scalable evaluation but cannot fully capture hardware-dependent behavior and fault propagation. Hardware-in-the-loop (HIL) validation bridges this gap by executing the driving stack on real embedded hardware within a simulated environment. However, preparing fault-augmented scenarios for HIL testing remains largely a manual process, limiting test coverage, scalability, and efficiency. We present FaultGene, an LLM-assisted framework for automated generation of executable fault-augmented tests for HIL fault-resilience validation. Given a baseline Scenic/CARLA scenario and a structured fault specification, FaultGene generates a fault-augmented test case that captures the effects of HIL-level fault injection. During offline refinement, semantic-consistency checks and execution-level feedback are used to correct candidates that fail to preserve the intended scenario context or produce the desired fault-induced degradation, and to construct high-quality supervision for fine-tuning. The resulting generator then produces fault-augmented tests directly at deployment time without requiring manually engineered reference executions or repeated refinement loops.

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arXiv AI Papershace 23 días

CulturalRiddles: A Multicultural Benchmark of Riddles

As LLMs expand their language coverage, evaluating how well they understand and represent diverse cultures becomes increasingly important. However, many benchmarks remain English-centric, with limited cultural coverage and a focus on factual knowledge over its application in real-world cultural contexts. We propose CulturalRiddles, a benchmark of original, culturally grounded riddles created entirely by humans through a participatory community effort, spanning 62 cultures (45 countries, 51 languages). These riddles are deeply rooted in their respective cultures and require abductive and compositional reasoning, alongside detailed factual and social knowledge, language skills, and an understanding of culturally specific associations. We benchmark 28 LLM configurations using both automatic and large-scale human evaluation. Even frontier models achieve at most 22% accuracy in some communities. We publicly release all artifacts alongside an open contribution process to expand the benchmark's linguistic and cultural coverage.

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arXiv AI Papershace 23 días

CORD: Conflict-Aware Optimization for Joint RL and Distillation in Agents

Joint reinforcement learning and self-distillation combines task rewards with dense teacher guidance for agent training, but their gradients can partly cancel. A mixing coefficient controls relative scale without selectively removing opposing components. We present CORD, a general framework for conflict-aware optimization of two objectives. CORD separates the choice of protected objective from the condition that triggers projection, and combines the gradients once per optimizer mini-batch. A nearest-point formulation yields a closed-form update and characterizes its first-order effect on the protected objective before optimizer transformations. Gradient diagnostics connect conflict in agent training to lost local optimization progress and motivate selective correction. As a gradient-combination mechanism, CORD can be added to different joint training recipes while retaining their objectives. Across three such recipes, it improves ALFWorld success by 1.5-15.9 percentage points. On WebShop, it improves two of the three pairs, with SDAR rising from 60.7% to 64.6% success. Our analyses explain why protection direction and projection gate must be chosen together, and why the distillation objective and its mixing weight remain consequential after projection.

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arXiv AI Papershace 23 días

Near Minimax Rate for Quantile Distributional Reinforcement Learning

In this paper, we study quantile distributional policy evaluation, where the return distribution under a given policy is approximated by the quantile Bellman fixed point η_m. We investigate the statistical accuracy of estimating η_m across quantile resolutions under Wasserstein distances. Assuming access to a generative model, we construct a quantile fixed point estimator η_m^{(n)} from an empirical Markov decision process and establish non-asymptotic error bounds under Wasserstein distances. When reward densities are uniformly bounded away from zero, we obtain a root-n high-probability bound simultaneously over all resolutions m and all Wasserstein orders 1p. When reward densities vanish at the support endpoints, we show that continuity and endpoint monotonicity suffice to preserve the same resolution-independent rate for 1p2. For p>2, we establish a root-n bound with coefficient m^{1/2-1/p}, together with a slower, resolution-independent n^{-1/p} bound for every finite p>2. We further obtain resolution-independent W_consistency governed by the continuity modulus of reward quantile functions. To investigate the sharpness of these bounds, we characterize the exact local asymptotic minimax risk at each fixed resolution as the expected Wasserstein-coordinate norm of the efficient Gaussian limit and prove that our estimator attains it. We then examine the resolution dependence of this optimal local risk. A regular one-state instance has resolution-independent local risk constants, whereas a single endpoint-vanishing two-state instance exhibits growth of order m^{1/2-1/p}, up to logarithmic factors along a resolution subsequence, for every p>2.

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arXiv AI Papershace 23 días

A Transfer Learning-Driven Methodology for Efficient and Sustainable Indoor Localization Using VLC

The rapid expansion of the Internet of Things enables seamless collaboration among connected devices, making indoor localization a critical component of industrial automation. However, conventional localization techniques struggle in dynamic environments. These methods depend on extensive, frequent data collection and environment-specific calibration, limiting scalability, interoperability, and effective use of prior research while imposing significant energy penalties that hinder practical deployment. To address these challenges, we propose an energy-efficient transfer learning (TL) based approach for visible light communication based indoor localization. Our method leverages TL to tackle environmental variability, including lighting fluctuations and physical obstacles that typically degrade localization performance, while simultaneously reducing computational overhead and energy consumption compared to conventional approaches. We also introduce a novel model efficiency (ME) metric, designed to integrate localization accuracy, energy efficiency, data efficiency, and transfer gain into a single evaluative measure for comprehensive optimization. Evaluations on a real-world dataset collected from a BOSCH factory demonstrate that our model achieves a 47% improvement in localization accuracy compared to a conventional model that does not utilize TL, and achieves up to 67.5% ME with TL models, compared to only 10.4% in conventional models under high-noise conditions. These results underscore the potential of our approach to deliver a highly efficient and scalable indoor localization system suitable for energy-constrained industrial applications.

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arXiv AI Papershace 23 días

Fragment-Level Graph Encoding for Interpretable Molecular Property Prediction

Graph neural networks (GNNs) are among the standard tools for molecular property prediction, but the atom-level explanations of their predictions are hard for medicinal chemists to act on. We propose a change of input representation rather than a new model: molecules are encoded as fragment-level graphs from BRICS decomposition, where each node is a synthetically meaningful fragment whose features are the element-wise sum of its atoms' features. Because only the representation changes, any message-passing GNN and any post-hoc attribution method produces explanations at the resolution of functional groups instead of single atoms. Across four architectures, four prediction tasks, and two explainers under scaffold splits, fragment-level encoding matches atom-level test accuracy on roughly five-fold smaller graphs. Integrated-Gradients attributions recover known pharmacophores above chance on two enzyme targets, namely BACE at 1.5 times the chance rate, and PTR1 at 3.2 times. A like-for-like comparison against an aggregated atom-level baseline shows that post-hoc aggregation is not a stable substitute: its BACE hit rate range both exceeds and falls below the fragment model's hit rate depending on two arbitrary aggregation choices, whereas fragment-level training yields a single convention-free attribution at no predictive cost. The code is available at: https://github.com/4D-Lab/frame

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arXiv AI Papershace 23 días

Unlocking Spatial Grounding in Flow-Matching Models via SNR Tuning

Despite the success of foundation models in image synthesis, achieving fine-grained spatial control remains a significant challenge, typically requiring auxiliary structural adapters or complex inference-time steering. While modern vision-language (VLM) text encoders possess the latent capacity to represent spatial coordinates, generative models often fail to leverage this information. We demonstrate that the signal-to-noise ratio (SNR) distribution is a critical determinant of spatial grounding in flow-matching models. Using a novel synthetic benchmark of geometric primitives, we isolate structural mechanics from textural complexity inherent in natural imagery and introduce a rigorous color-aware layout metric to quantify spatial fidelity. We show that training on a centered SNR distribution results in poor layout adherence (25.9%). Instead, we identify high-noise regimes as a "critical learning phase" for spatial grounding. Shifting the training focus toward these stages increases layout adherence to 80.8%, effectively reaching the 83.6% reconstruction ceiling of the underlying variational autoencoder. We find that the necessity for this shift scales with resolution and aspect ratio variance, and that the conservative noise schedules employed by models like FLUX.1 are insufficient for precise layout adherence. Finally, we show that these findings translate to real-world imagery via the COCO dataset (1.0 vs. 57.3 Average Precision), where state-of-the-art layout adherence is unlocked through distribution tuning alone, without architectural modifications.

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arXiv AI Papershace 23 días

Fast Bayesian Optimization Through Linear Models

Bayesian optimization (BO) is typically used only for problems where the underlying function is deemed "expensive" (e.g. in time or money), with the speed of the BO algorithm being of little importance. In the real world, however, problems span a continuous range of values, going from cheap to expensive. In this paper, we present BO algorithms through the lens of a speed-performance Pareto frontier: we compare BO against faster algorithms like CMA-ES, and highlight how certain regions of the Pareto frontier are currently empty. As a solution, we propose a new set of fast BO methods that exploit the linear structure in existing BO algorithms, for both continuous and combinatorial inputs (from ~10 to 6000+ dimensions). Our methods achieve performance similar to state-of-the-art BO, while being more than 10^2to 10^3faster, opening the field of BO to an entirely new class of problems.

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arXiv AI Papershace 23 días

What Prediction-Market Contracts Reveal at Launch: Trading Volume and Dispute Risk

Using a near-census of 802,863 resolved markets listed on Polymarket between January 2023 and May 2026, and only information available at listing (the market's title and description, named resolution source, and contract metadata), we predict lifetime trading volume and whether settlement is later disputed. Contract information predicts trading volume well (out-of-sample R^2 = 0.747 for log volume), and most of the signal is interpretable: market topic provides the largest gain (+0.289 R^2 over a 0.220 contract-feature baseline), wording and resolution-rule design add smaller but measurable improvements (+0.027 combined), and neural text models add little beyond the interpretable features. This performance does not survive a period of platform change coinciding with a shift in listing formats: accuracy in predicting whether a market reaches \1,000 falls from AUC 0.940 under same-period evaluation to 0.577$ when training on past quarters and testing on later ones. The loss concentrates in markets whose question forms were absent from training (76.9% of test markets; on familiar forms the model still reaches 0.778), and a decomposition attributes more of it to a changed relationship between contract characteristics and demand than to an outdated mapping from text to features. Dispute predictors are more stable but less precise: launch-time features rank dispute risk far better than chance (AUC 0.78), yet at a 0.31% base rate precision is too low for case-by-case prediction (average precision 0.012), so the scores suit ranking groups of markets rather than forecasting individual disputes.

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arXiv AI Papershace 23 días

The Reflexivity Threshold: A Phase Transition for Multi-Agent Performative Prediction

Many learning systems are performative: once deployed, their predictions change the data distribution on which future models are trained. In multi-agent settings, this feedback is routed through a network of interacting learners, so stability depends not only on the strength of performativity but also on who affects whom. We study repeated retraining in multi-agent performative prediction and identify a spectral threshold that separates stable learning from worst-case learning obstruction. Prior work often collapses cross-agent feedback into a scalar contraction constant, missing this network structure. We instead introduce the reflexivity matrix Γ, which records how strongly one agent's deployment can move another agent's best response, defined from per-agent gradient sensitivities and curvature parameters. The threshold occurs when the spectral radius of this matrix crosses one. Below the threshold, repeated retraining converges geometrically to a unique performatively stable equilibrium, with cumulative excess loss bounded uniformly in the time horizon. Above the threshold, we construct noisy supercritical instances in which every local-information learner incurs expected cumulative excess loss growing linearly for horizons up to order 1/(ρ(Γ)-1). Together, these results characterize the threshold behavior through upper and lower bounds and recover the single-agent scalar threshold from prior work as a special case.

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arXiv AI Papershace 23 días

Private Online Prediction from Experts with Small Losses

We consider online prediction from experts, a fundamental problem in machine learning, under differential privacy constraints. Existing private algorithms achieve near-optimal regret {O}({T}), where T is the time horizon. However, this bound becomes suboptimal when L^, the cumulative loss of the best expert, is significantly less than T. In this work, we present the first differentially private algorithm with regret {O}({L^}), offering a substantial improvement in the low-loss regime. Our regret bound matches the non-private lower bound up to poly-logarithmic factors for constant privacy parameters, demonstrating that privacy incurs only a small cost in this setting. Our result assumes that the input sequence is oblivious, i.e., it is fixed in advance and independent of the algorithm's outputs. We also prove that an Ω({T}) regret is inevitable for adaptively generated inputs, even when L^= 0.

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arXiv AI Papershace 23 días

Implicit Feature Reweighting for Label-Free Graph Generalization

Graph-level out-of-distribution (OOD) generalization is critical for deploying graph neural networks in real-world applications where distribution shifts are inevitable. Existing approaches typically require class labels during representation learning to decide which substructures are predictive, limiting their applicability. We propose SCARCE, a label-free representation-learning framework that yields distribution-shift-robust graph representations from an environment partition of the training graphs alone, whether that partition comes from dataset metadata or, when none is available, from clustering a self-supervised embedding. Our method operates in two stages: (1) a variational graph autoencoder is warm-started for graph reconstruction; and (2) an adversarial confusion mechanism forces the encoder to produce representations from which a discriminator cannot distinguish environments, while preserving graph-reconstruction fidelity. We provide theoretical analysis showing that environment confusion contracts environment-discriminative directions in the representation space, which, under regularized downstream classifiers, is equivalent to imposing a stronger directional penalty on environment-associated features. This feature-reweighting mechanism reduces reliance on spurious correlations without erasing information, offering a label-free, continuous relaxation of last-layer reweighting methods. Experiments on a controlled synthetic datasets and the DrugOOD datasets show that SCARCE achieves state-of-the-art OOD generalization among label-free methods, using no class labels during representation learning.

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arXiv AI Papershace 23 días

Certified Neural Approximation of Robust Nonlinear Model Predictive Control

Model predictive control is a high-performance control method, but it incurs comparatively high online computational costs across many control tasks and is challenging to realize in low-level hardware settings. To address this, we present a framework for generating neural network controllers that provably inherit the safety and stability guarantees of a robust model predictive policy. In contrast to approaches based on post-verification or additional online safety filters, our method provides constructive, a priori design conditions that ensure preservation of recursive feasibility and stability before deployment. We treat the approximation error as a bounded input disturbance, which is addressed in the MPC design. This allows the neural network controller to directly inherit robustness from the underlying model predictive controller, which enables the transfer of guarantees to the approximation. The design process is an offline, constructive synthesis pipeline and can be implemented once Lipschitz bounds are specified. We further demonstrate in simulation that the learned controller reproduces the closed-loop behavior of the original controller while reducing online computation.

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