RAC adds a log-based safety net to AI agents via framework extensions, delivering 1.5-8X better latency and token use than LLM-based recovery on complex problems in τ-bench and REALM-Bench.
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6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 6representative citing papers
A new benchmark shows LLM smartphone agents achieve comparable success with screen text alone as with screenshots, but both fail often due to UI accessibility and reasoning gaps.
YoloFS is an agent-native filesystem that stages mutations for review, provides snapshots for agent self-correction, and uses progressive permissions to reduce user interruptions while matching baseline task success.
Supervised fine-tuning with 0.1% labeled data outperforms all 60 tested prompt variants for CLIPSeg cloud segmentation on satellite imagery under domain shift.
Riemann-Bench is a private benchmark of 25 research-level math problems on which all tested frontier AI models score below 10%.
Compiled AI generates deterministic code artifacts from LLMs in a one-time compilation step, enabling reliable workflow execution with zero runtime tokens after break-even.
citing papers explorer
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Robust Agent Compensation (RAC): Teaching AI Agents to Compensate
RAC adds a log-based safety net to AI agents via framework extensions, delivering 1.5-8X better latency and token use than LLM-based recovery on complex problems in τ-bench and REALM-Bench.
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Do LLMs Need to See Everything? A Benchmark and Study of Failures in LLM-driven Smartphone Automation using Screentext vs. Screenshots
A new benchmark shows LLM smartphone agents achieve comparable success with screen text alone as with screenshots, but both fail often due to UI accessibility and reasoning gaps.
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Don't Let AI Agents YOLO Your Files: Shifting Information and Control to Filesystems for Agent Safety and Autonomy
YoloFS is an agent-native filesystem that stages mutations for review, provides snapshots for agent self-correction, and uses progressive permissions to reduce user interruptions while matching baseline task success.
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Low-Data Supervised Adaptation Outperforms Prompting for Cloud Segmentation Under Domain Shift
Supervised fine-tuning with 0.1% labeled data outperforms all 60 tested prompt variants for CLIPSeg cloud segmentation on satellite imagery under domain shift.
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Riemann-Bench: A Benchmark for Moonshot Mathematics
Riemann-Bench is a private benchmark of 25 research-level math problems on which all tested frontier AI models score below 10%.
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Compiled AI: Deterministic Code Generation for LLM-Based Workflow Automation
Compiled AI generates deterministic code artifacts from LLMs in a one-time compilation step, enabling reliable workflow execution with zero runtime tokens after break-even.