PROTEA supplies an offline interface for scoring intermediate outputs in multi-agent LLM workflows, performing backward evaluation from final answers, and iterating on targeted prompt revisions with visible score changes.
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9 Pith papers cite this work. Polarity classification is still indexing.
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ContractBench shows that LLM agents frequently violate observation contracts by using expired artifacts or corrupting their byte integrity, with no model exceeding 80% success and notable scaling irregularities across families.
CrackMeBench introduces 20 deterministic binary validation tasks and reports GPT-5.5 solving 11/12 generated ones at pass@3 while Claude and Kimi lag, especially on harder tasks.
FORTIS benchmark shows over-privilege is the norm in LLM agent skill selection and execution, with models reaching for higher-privilege skills and tools than required across ten frontier models and three domains.
A separate memory agent that selectively injects reminders into an unmodified action agent improves long-horizon task performance by up to 8.3 percentage points.
TokenWall mediates persistent-agent security by auditing source–sink token flows with a local small model and selective large-model escalation, cutting CIK-Bench attack success to 12.5% at low benign latency.
A structured Research Question Certificate improves the judged quality and auditability of LLM-proposed scientific questions, per a small LLM-judge evaluation.
A 4B compiler model generates LoRA adapters from natural-language specs, enabling a frozen 0.6B interpreter to match Qwen3-32B performance on fuzzy text tasks at 50× less memory.
LLMs fail at extended counting of repeated characters due to finite internal states, with abrupt errors persisting across model scales and inference methods.
citing papers explorer
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PROTEA: Offline Evaluation and Iterative Refinement for Multi-Agent LLM Workflows
PROTEA supplies an offline interface for scoring intermediate outputs in multi-agent LLM workflows, performing backward evaluation from final answers, and iterating on targeted prompt revisions with visible score changes.
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ContractBench: Can LLM Agents Preserve Observation Contracts?
ContractBench shows that LLM agents frequently violate observation contracts by using expired artifacts or corrupting their byte integrity, with no model exceeding 80% success and notable scaling irregularities across families.
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CrackMeBench: Binary Reverse Engineering for Agents
CrackMeBench introduces 20 deterministic binary validation tasks and reports GPT-5.5 solving 11/12 generated ones at pass@3 while Claude and Kimi lag, especially on harder tasks.
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FORTIS: Benchmarking Over-Privilege in Agent Skills
FORTIS benchmark shows over-privilege is the norm in LLM agent skill selection and execution, with models reaching for higher-privilege skills and tools than required across ten frontier models and three domains.
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Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
A separate memory agent that selectively injects reminders into an unmodified action agent improves long-horizon task performance by up to 8.3 percentage points.
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Token-Flow Firewall: Semantic Runtime Auditing for Persistent AI Agents
TokenWall mediates persistent-agent security by auditing source–sink token flows with a local small model and selective large-model escalation, cutting CIK-Bench attack success to 12.5% at low benign latency.
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FirstResearch: Auditable Question Formation for LLM Scientific Discovery Agents
A structured Research Question Certificate improves the judged quality and auditability of LLM-proposed scientific questions, per a small LLM-judge evaluation.
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Program-as-Weights: A Programming Paradigm for Fuzzy Functions
A 4B compiler model generates LoRA adapters from natural-language specs, enabling a frozen 0.6B interpreter to match Qwen3-32B performance on fuzzy text tasks at 50× less memory.
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Language models fail at extended rule following
LLMs fail at extended counting of repeated characters due to finite internal states, with abrupt errors persisting across model scales and inference methods.