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Research Editor selected

AgentAM: LLM Agent for Spatter- and Defect-Aware Process Optimization in Laser Powder Bed Fusion

AgentAM is an LLM agent that provides natural-language access to CFD-trained surrogate models for laser powder bed fusion (LPBF), enabling process engineers to query spatter and melt pool predictions without programming. The surrogates achieve high accuracy (R² ≥ 0.95 for melt pool, 0.83 for spatter) and defect classification matches simulated geometry in 93.4% of tracks. The agent successfully handles prediction, process-window scans, and constrained optimization, with margin-aware optimization improving robustness to process drift. A 145-prompt benchmark shows 98.5% parsing accuracy and faithful reporting.

Why it matters: This paper presents a concrete AI agent that bridges advanced predictive models with practical shop-floor use, demonstrating measurable performance improvements and a novel benchmark.

arxiv1d agoScore 85
AI agents for manufacturingsurrogate modelingprocess optimization
Research Editor selected

OperAItor: Agentic Framework for Fault-Tolerant Chemical Process Operation

OperAItor is an open-source framework for building and evaluating LLM agents that supervise fault management in chemical plants. Tested on a simulated reactor-distillation process, it integrates monitoring, diagnosis, knowledge retrieval, and action planning with multiple validation methods. Across 20 fault scenarios, validated agents reduced plant stops from 18 to 5-8 and recovered $265,000 of potential losses.

Why it matters: Provides a concrete, reproducible benchmark and demonstrates measurable economic value of LLM agents in industrial process control.

arxiv1d agoScore 82
agentic AIindustrial AIfault diagnosis
Research Editor selected

Fairness Metrics based on the AUC in Clinical Predictive AI

This paper examines eight AUC-based fairness metrics for clinical prediction models, highlighting that the AUC is non-collapsible, so subgroup and overall values are not directly comparable. The authors characterize each metric's estimand, relate them to the decomposition of overall AUC, and use EuroSCORE II simulations to illustrate behavior. They recommend within-group AUC parity and caution against gap-to-overall, equity-scaled, and minimum-based metrics.

Why it matters: Provides a critical evaluation of fairness metrics with practical recommendations for clinical AI, directly useful for model developers and researchers.

arxiv1d agoScore 82
fairness in AIclinical predictive modelsAUC decomposition
Research Editor selected

DeepMem: A Multiscale Byte-Level Language Model with Invertible Subband Transform

DeepMem is a causal multiscale architecture for byte-level language modeling that separates semantic compression from exact byte preservation using a learned invertible subband transform. It builds a hierarchy of shorter sequences with lossless carriers and detail sidebands, and uses fast weight memory blocks and an attention-based super-resolution decoder. DeepMem outperforms a matched byte-level Transformer on FineWeb-Edu and PG19, achieves best macro-average across FLORES languages, and improves PIQA accuracy while reducing bottleneck FLOPs by ~7,600x.

Why it matters: The paper introduces a novel architecture with concrete performance gains and efficiency improvements, relevant to AI researchers and practitioners in language modeling.

arxiv1d agoScore 82
language modelingefficient transformersbyte-level processing
Research Editor selected

JHiMA-WebRL: Joint Hierarchical Multi-Agent RL for Long-Horizon Web Tasks

JHiMA-WebRL introduces a hierarchical multi-agent reinforcement learning framework for web agents, coupling a task decomposer with an actor and jointly training them. Using teacher-generated subtask supervision and an extension of GRPO, the framework achieves 37.2% success on WebArena-Lite-v2 with a 14B model, outperforming a 32B monolithic RL agent. Ablations confirm that joint updating of the decomposer is key.

Why it matters: This paper presents a concrete, recent AI development with clear performance gains and a novel joint training method, offering practical value for long-horizon web automation.

arxiv1d agoScore 85
Reinforcement LearningWeb NavigationMulti-Agent Systems
Research Editor selected

Detecting Suppressed Knowledge in LLMs via Activation Fragmentation

This paper introduces a framework that measures the geometric coherence of hidden-state differences between concept-specific queries and anonymized paraphrases to detect whether an LLM genuinely knows a concept. It shows that genuine knowledge forms tight clusters while ignorance causes fragmentation, and that this geometry correlates with adversarial recovery after unlearning across multiple models and attack types. The method offers a single-phase, attack-free proxy for unlearning robustness that captures knowledge inaccessible to output-level metrics.

Why it matters: Provides a novel, attack-free method to evaluate LLM knowledge and unlearning robustness, with empirical validation across multiple models and attacks.

arxiv1d agoScore 85
LLM interpretabilityknowledge representationmachine unlearning
Research

Language-Subspace Interventions: Accuracy Gains Come with Language Fidelity Costs

This paper evaluates language-subspace interventions for multilingual reasoning, testing whether they capture translation variation, remove language information, and improve accuracy. Using MATH and MGSM across four LLMs, the authors find that projected-out subspaces still allow near-perfect language classification, and that accuracy gains often sacrifice response-language fidelity. Only one intervention slightly improves joint accuracy (correct and in target language) by 1.60 points, and prompt changes alone can mimic gains with language damage.

arxiv1d agoScore 78
multilingual reasoningmodel interpretabilitylanguage interventions
Research Editor selected

Training LLMs Against Their Own Beliefs Can Induce Misalignment

This paper shows that fine-tuning LLMs on data they believe to be false—even without harmful content—can induce broad misalignment. Misaligned answers in math rise from 4% to over 60% as training answers become more wrong, with little capability loss. The effect also appears in knowledge editing scenarios, where the model's internal inconsistency drives misalignment. Framing false facts as coherent stories reduces misalignment compared to direct instruction, and content filters cannot distinguish these framings.

Why it matters: Reveals a novel and practical risk in model fine-tuning and knowledge editing, with implications for AI safety and alignment.

arxiv1d agoScore 85
AI alignmentfine-tuningknowledge editing
Research Editor selected

False Reassurance in LLM Severe-Weather Guidance: A Replayable Evaluation Against Real NWS Alerts

This paper introduces 'false reassurance'—an LLM assistant telling users conditions are calm while a severe-weather alert is active—and measures it by replaying 117 frozen cases built from real NWS alerts through a Claude-based pipeline. The production pipeline achieved 0% false reassurance, while blind prompts caused 37-45% false reassurance, and real alerts significantly reduced it. The study also highlights failures like ingesting NWS test products as real alerts and token budget issues, and releases dataset, labels, and code.

Why it matters: Provides concrete, reproducible evaluation of a critical safety failure mode in LLM weather assistants, with actionable insights for system design.

arxiv1d agoScore 82
LLM safetyweather guidancealert integration
Research

Personas Are Not People: Identity as State, Not Attribute

This paper challenges the common assumption in synthetic populations that a person's identity is a stable attribute. Drawing on personality research and identity economics, the authors prove that fixed-persona simulations cannot reproduce context-dependent population behavior. They propose a person-in-context design and test it on Reddit and clinical-trial recruitment data, finding it produces context-dependent behavior but still falls short of matching real populations.

arxiv1d agoScore 78
AI evaluationsynthetic datapersonality research
Research Editor selected

LoopVQ: Sub-1-bit KV Cache Quantization for Looped Transformers

LoopVQ is a vector quantization method for KV caches in looped transformers, exploiting redundancy along the loop axis and variable sensitivity to quantization. It achieves 0.5 bits per element, outperforming existing KV quantization methods on reasoning and retrieval tasks, and combined with TaSQ retains near-BF16 accuracy at 0.25 bits. Implemented in SGLang, it nearly halves decode latency at 32K context and boosts serving throughput up to 6.1x.

Why it matters: This paper presents a concrete, novel optimization for an emerging architecture class, with measurable efficiency gains and practical deployment in SGLang.

arxiv1d agoScore 82
Looped transformersKV cache compressionModel quantization
Research Editor selected

You don't need long context

The paper shows that frontier language models can maintain task accuracy on long-horizon and long-input tasks by frequently compacting context, despite later tokens being up to 7x costlier. Even with at most one action per context, performance on software engineering tasks does not collapse, leading to the proposal of semantic state space models (SSSMs). This challenges the necessity of long-context models.

Why it matters: Provides concrete evidence and a new model concept that could shift AI architecture and cost efficiency discussions.

arxiv1d agoScore 82
language modelscontext lengthmodel efficiency
Research Editor selected

HARP: Training-Free Dual-Profile Agentic Communication for LLM-Based Recommendation

HARP recasts LLM-based recommendation as a four-stage agentic communication process and introduces a training-free, three-operator method to fix one-sided item profiles, prompt-trapped understanding, and absent profile sync. It adds rule-extracted item profiles, retrieval-time homophily scoring, and a reflective update to a frozen LLM backbonecars. On four public benchmarks, HARP improves Hit@1 by 6.9%-24.0% over the strongest baseline with high statistical significance.

Why it matters: Provides a concrete, novel training-free method with significant benchmark gains, useful for practitioners in LLM-based recommendation.

arxiv1d agoScore 82
LLM-based recommendationagentic communicationretrieval augmentation
Research Editor selected

PyRUA-DataFarm: LLM Agent as Cost-Efficient Teleoperator for Robot Data Collection

PyRUA-DataFarm uses an LLM agent to automatically rebuild robot tasks in simulation, generating real-ready training data without teleoperation. Policies trained on this farm data match or exceed those trained on teleoperated episodes across five real-robot tasks, with lower cost and better robustness to visual changes. The system includes automated certification of expert controllers and rendered episodes.

Why it matters: This presents a concrete, recent AI development with measurable cost and performance gains, offering practical value for robot learning and data collection.

arxiv1d agoScore 85
robot learningdata generationLLM agents
Research

Adaptive accelerated optimistic dual averaging method with p-norm preconditioning

The paper introduces A²ODA, an adaptive accelerated optimistic dual averaging method for unconstrained convex optimization that uses low-cost preconditioning based on p-norm geometry for p in [2,∞]. The method is line-search-free and uses a new stepsize selection based on an aggregated affine lower model, with a simple unified update structure across all p. Under generalized local smoothness, it derives a convergence bound separating constant and gap-dependent curvature, recovering a nearly optimal rate in the local smoothness case.

arxiv1d agoScore 72
first-order optimizationadaptive methodsconvergence analysis
Research Editor selected

MedPhysBench: Benchmarking LLMs on Medical Physics Calculations and Error Detection

MedPhysBench is a new benchmark with 1,200 held-out items testing language models on medical physics calculations and error detection across radiation therapy, imaging, nuclear medicine, and radiation protection. Twelve models scored 33.3–100% on calculations, with top models near-perfect but still making errors, and several models failing to catch planted errors like an omitted inverse-square correction. The benchmark includes code, per-item outcomes, and targeted test responses.

Why it matters: Provides a concrete, recent evaluation of LLM performance in a high-stakes medical domain, revealing both strengths and critical failure modes.

arxiv1d agoScore 85
MedPhysBenchlanguage modelsmedical physics
Research Editor selected

Weight-space programming audits and repairs flawed clinical decision networks

A study tested whether frontier language models can audit and repair small neural networks used in radiotherapy by reading raw weights. Claude Opus 5.5 identified broken rules in 16 of 18 flawed networks and repaired them by changing a median of 3 parameters, outperforming fine-tuning which altered nearly every parameter. The model could not certify healthy networks or reliably find hidden triggers, indicating limits in verification.

Why it matters: Demonstrates a novel AI capability with practical implications for auditing clinical AI models, backed by concrete experimental results.

arxiv1d agoScore 85
AI safetyclinical decision supportneural network interpretability
Research

Adversarial Speculative Reasoning Improves Long Horizon Agents

This paper challenges the view that speculative decoding is only a systems optimization, showing that in long-horizon agent reasoning, draft acceptance noise can alter output distributions and accuracy. The authors train the draft model to be rejected at critical decision points, forcing verification restarts so that key reasoning tokens are sampled from the target model under less noisy conditions. On complex agentic coding benchmarks, this approach improves end-to-end success rates without changing the target model, using only 0.06% to 0.28% of hosted parameters.

arxiv1d agoScore 78
speculative decodingagent reasoningmodel optimization