arXiv AI
Upstream Influence, Downstream Re-Anchoring in LLM Agent Chains
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Manufactured Confidence: How Memory Consolidation Turns Hearsay into Confident Facts
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How Strongly Should Task State Influence an LLM Agent?
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When "Must" Becomes "Maybe": Erosion of Workflow Constraints in LLM Agent Handoffs
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arXiv AI
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Adversarial Feeds Steer LLM Agent Decisions Against Their Defaults
arXiv:2606. 00914v1 Announce Type: new Abstract: LLM agents increasingly act after consuming ranked external information streams such as social feeds, search results, retrieval contexts, and email queues, yet safety evaluations almost always test the model or the user prompt in isolation, never the upstream ranker that decides what the agent reads just before it acts.
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Grounded Continuation: A Linear-Time Runtime Verifier for LLM Conversations
Grounded Continuation introduces a runtime verifier that classifies each utterance in an LLM conversation into one of eight epistemic operations and uses a symbolic engine to maintain a dependency map of claims and their supports. The verifier checks whether a new continuation is grounded by walking this map, a linear-time process that requires no additional LLM calls. On benchmarks such as ReviseQA and MemoryAgentBench, the verifier improves single-hop accuracy for several QA models, even enabling a 7B model to outperform GPT‑4o when guided by the verifier.
arXiv AI
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Counterfactual Evidence Audits Predict LLM-Agent Susceptibility to Ranked Context
The paper introduces a counterfactual evidence audit to assess how large language model agents respond to different sets of documents. By exposing agents to two mirrored sets of five documents and measuring changes in six downstream decisions, the authors predict how the agents will behave when presented with a full 45‑document context. Across multiple open‑weight model families and tasks, the five‑document audit accurately forecasts full‑context effects, reducing prediction error by 62% and correctly identifying the direction of most material effects.