Home Knowledge Base the Diagnosis Layer

the Diagnosis Layer

No mentions found

This entity hasn't been tracked yet, or Iris is still building its knowledge base.

Related Articles from SNS

Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents

arXiv:2605.08717v2 Announce Type: replace Abstract: Software engineering agents are increasingly deployed in evaluable engineering environments, yet post-failure recovery remains costly, manual, and ad hoc. Existing systems expose traces or generate follow-up feedback, but they do not convert heterogeneous runtime evidence into grounded, bounded recovery guidance for a subsequent attempt. We present PROBE, a failure-anchored framework for structured recovery in software engineering agents.

arXiv CS 2d ago

Three reports, one story: MiraOne to usher in new era of precision medicine through single blood test; grand launch in Mumbai

MUMBAI: MiraOne - a comprehensive blood test that promises to transform the way we study our body - was launched on Saturday in Mumbai at ImagiNXT conference attended by an impressive audience. MiraOne, which has been conceived on the principle of “One test, One you” generates three reports to tell one story from a single draw of blood. It gives a complete picture - your blood markers, the DNA you inherited and how you respond to medicines.

Times of India 18d ago

Capacity-Controlled Global Attention for Graph Transformers

arXiv:2604.17324v2 Announce Type: replace Abstract: Global self-attention drives modern graph transformers, yet the softmax at its core imposes a structural constraint rarely examined directly: every attention row is non-negative and sums to one, so each per-head output is a mass-conserving convex combination of value vectors. A node can never "attend to nothing." We argue this conservation constraint is a single root cause behind three pathologies usually studied in isolation: the collapse...

arXiv CS 1d ago

Agentic multi-fidelity learning of quasiparticle and excitonic properties

arXiv:2606.07836v1 Announce Type: cross Abstract: Many-body GW-Bethe-Salpeter equation calculations are essential for accurate simulations of electronic structure and optical properties in modern low-dimensional nanomaterials. However, these methods are computationally demanding and can exhibit localized numerical instabilities or convergence failures that are difficult to detect within high-throughput workflows. We introduce an agent-guided multi-fidelity framework for correcting...

arXiv CS 1d ago

Agentic multi-fidelity learning of quasiparticle and excitonic properties

arXiv:2606.07836v1 Announce Type: cross Abstract: Many-body GW-Bethe-Salpeter equation calculations are essential for accurate simulations of electronic structure and optical properties in modern low-dimensional nanomaterials. However, these methods are computationally demanding and can exhibit localized numerical instabilities or convergence failures that are difficult to detect within high-throughput workflows. We introduce an agent-guided multi-fidelity framework for correcting...

arXiv Physics 1d ago

QoEReasoner: An Agentic Reasoning Framework for Automated and Explainable QoE Diagnosis in RANs

Announce Type: replace Abstract: Diagnosing Quality-of-Experience (QoE) degradations in operational Radio Access Networks (RANs) is a critical but notoriously complex task, traditionally requiring labor-intensive expert analysis over high-dimensional, cross-layer telemetry. While Large Language Models (LLMs) offer unprecedented reasoning capabilities, they are fundamentally unsuited for raw RANs troubleshooting: they fail at numeric time-series analysis, hallucinate protocol-violating causal...

arXiv CS 7d ago

QoEReasoner: An Agentic Reasoning Framework for Automated and Explainable QoE Diagnosis in RANs

arXiv:2606.01925v1 Announce Type: new Abstract: Diagnosing Quality-of-Experience (QoE) degradations in operational Radio Access Networks (RANs) is a critical but notoriously complex task, traditionally requiring labor-intensive expert analysis over high-dimensional, cross-layer telemetry. While Large Language Models (LLMs) offer unprecedented reasoning capabilities, they are fundamentally unsuited for raw RANs troubleshooting: they fail at numeric time-series analysis, hallucinate...

arXiv CS 8d ago

Lying Is Just a Phase: The Hidden Alignment Transition in Language Model Scaling

arXiv:2605.18838v3 Announce Type: replace Abstract: Scaling laws predict loss from compute but not how capabilities interact. We measure the coupling between reasoning and truthfulness across 63 base models from 16 families and find a regime change invisible to loss curves: below a family-dependent critical scale N_c, capabilities anticorrelate (r = -0.989, p = 4 x 10^{-5} nonparametric permutation test); above it, they cooperate.

arXiv CS 8d ago

UModel: An Agent-Ready Observability Data Modeling Method at Scale

arXiv:2606.04799v1 Announce Type: new Abstract: When networked system failures occur, automatically performing Root Cause Analysis (RCA) using observability data is critical for ensuring networked system reliability. Recently, LLM-based agents have shown promise for automating this diagnosis process through advanced reasoning and autonomous exploration.

arXiv CS 6d ago

ResNet-34 with Lightweight Decoder for Accurate and Efficient Segmentation of Fetal Brain MRI

arXiv:2606.01293v1 Announce Type: cross Abstract: Accurate segmentation of fetal brain tissues in Magnetic Resonance Imaging (MRI) is critical for early diagnosis of congenital abnormalities and improving prenatal care. However, the task remains difficult because of fetal motion, low tissue contrast, and major anatomical variability throughout gestational ages, particularly in segmenting complex structures such as white matter, gray matter, lateral ventricles, deep gray matter,...

arXiv CS 8d ago