3arXiv cs.LG (Machine Learning)·
MILD framework: proactive multi-intent failure prediction and root-cause disambiguation for autonomous networks
Researchers introduce MILD, a framework that reframes network intent assurance from reactive drift detection to proactive failure prediction in self-driving (autonomous) networks. MILD uses a teacher-augmented Mixture-of-Experts architecture with a hybrid objective that jointly optimizes failure prediction and root-cause attribution across three operational macro-intents (telemetry, analytics, actuation). The system incorporates SHAP-based KPI-level diagnostics and multi-horizon urgency estimation, evaluated across statistical benchmarks, microservices environments, and an SDN-based edge-to-cloud testbed. The work is relevant to AI-driven infrastructure automation and closed-loop network management.
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AIMS dataset and intent-aware training improve LLM safety classification across multiple regimes
Researchers introduce AIMS, a 1,724-sample human-annotated dataset of difficult safety prompts paired with intent descriptions and harm labels, designed to study intent-aware training for LLM safety classifiers. The paper evaluates intent-aware training across SFT, DPO, reasoning distillation, and GRPO reinforcement learning, finding that directly rewarding intent faithfulness via GRPO yields the strongest average performance across five external safety benchmarks. Intent-conditioned distillation also outperforms reasoning-only distillation in most teacher-student pairs, and intent-aware models form the inference latency-F1 Pareto frontier. The work argues that explicit user intent modeling is a compact, high-quality supervision signal for more robust safety classification.
Five-layer framework for hidden safety-critical failures in deployed AI systems
A preprint from arXiv argues that AI safety discourse over-indexes on visible, model-centric failures while neglecting quieter systemic risks in deployed socio-technical systems. The authors propose a five-layer diagnostic framework covering epistemic, control, temporal, organizational, and ecosystem integrity. The paper identifies under-recognized risk patterns including uncertainty laundering, prompt injection, memory poisoning, evaluation deception, and model collapse, and calls for a shift from model-centric evaluation toward socio-technical reliability. It concludes with design, governance, and research agenda recommendations.
MANTA: Framework for self-evolving multi-agent communication topology at inference time
MANTA is a new framework that allows multi-agent LLM systems to dynamically adapt their communication topology during inference rather than treating it as a fixed design choice. The system initializes a task-conditioned topology from prior structural experience, then monitors collaboration traces and applies bounded structural updates—modifying agent roles, communication links, execution order, and validation pathways—as needed. Evaluated across five benchmarks spanning information seeking, tool use, planning, workflow execution, and mathematical reasoning, MANTA achieves an average score of 74.0, outperforming the strongest baseline by 5.8 percentage points.
ProtoAda: Prototype-Guided Adaptive Adapter Expansion for Multimodal Continual Instruction Tuning
ProtoAda is a new framework for Multimodal Continual Instruction Tuning (MCIT) that addresses a key failure mode in sparse Mixture-of-LoRA-Experts architectures: image-text similarity routing is format-blind and incorrectly merges tasks with similar semantics but different output structures (e.g., coordinate prediction vs. VQA). The method introduces format-aware task prototypes to guide both routing and adapter expansion, then consolidates compatible updates geometrically to reuse and refine existing parameters. Experiments across multiple benchmarks show improved performance, particularly on tasks whose answer formats are vulnerable to corruption by sequential fine-tuning.
Lost in Fog: Sensor Perturbations Expose Reasoning Fragility in Driving VLAs
This paper presents a controlled robustness study of Vision-Language-Action (VLA) models in autonomous driving, evaluating Alpamayo R1 (10B parameters) across ~18,000 inference trials under eight sensor perturbation types including noise, lighting extremes, and fog. The key finding is that Chain-of-Causation (CoC) reasoning consistency is a high-fidelity proxy for trajectory reliability: when CoC explanations change post-perturbation, trajectory deviation spikes 5.3× (r=0.99 across attack types). Enabling CoC generation is associated with 11.8% average improvement in trajectory accuracy, and degradation under noise is approximately linear (R²=0.957), while standard preprocessing defenses offer only marginal benefit.
Mechanism-driven internal monitors detect LLM training instability thousands of steps before loss divergence
A new arXiv preprint proposes mechanism-driven monitoring signals derived from the functional roles of critical modules (low-precision flash attention, MoE routers) to detect training instability before it manifests in loss or gradient norms. The authors derive monitors such as spectral entropy of a QK bilinear decomposition and MoE router indicators, showing via fault-injection experiments that these signals trigger thousands of steps ahead of loss divergence. The work targets a high-cost failure mode in frontier LLM training where instability can persist undetected for thousands of steps on expensive accelerator fleets.
CRAM: Centroid-Routing and Adaptive MoE for Multimodal Continual Instruction Tuning
CRAM is a new method for Multimodal Continual Instruction Tuning (MCIT) that addresses the tension between catastrophic forgetting and parameter efficiency in MLLMs. It combines adaptive-rank instantiation to dynamically allocate parameters based on capability gaps, centroid-guided routing to reuse existing expert knowledge, and an orthogonality penalty to confine new updates to task-specific directions. The approach uses a Mixture-of-Experts architecture where task-specific patterns are isolated into independent modules, avoiding both the interference of shared updates and the parameter bloat of fully isolated expansion. Experiments across diverse benchmarks show consistent improvements over existing MCIT methods.
Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents
Agentic CLEAR is an automatic evaluation framework for LLM-based agentic systems that analyzes behavior at three granularity levels: system, trace, and node. Unlike existing tools that rely on static error taxonomies or focus only on observability, it dynamically generates textual insights and integrates above the observability layer with an accessible UI. Experiments across four benchmarks and seven agentic settings demonstrate strong alignment with human-annotated errors and predictive accuracy for task success rates.