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5 results

Journal article

Pooling Experience Spends Independence Twice: Shared Memory in LLM Agent Teams Correlates Errors and Opens a Common-Mode Channel, Why Little of the First Was Left to Spend, and the Order, Gate and Readout That May Decide Whether Pooling Pays

Pranay Mahendrakar 2026

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Teams of language-model agents increasingly read and write a common memory: a message pool, a blackboard, a bank of distilled experience, a knowledge base served to several frameworks at once. The case for pooling is efficiency, since no agent…

Cite Related research LOR-00078 · 10 views · 13 downloads Select “Pooling Experience Spends Independence Twice: Shared…” for export

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Journal article

A Trust Score Needs a Consumer: Four Places a Per-Source Trust Value Can Act on an LLM Agent, the Missing Test of a Graded Discount Against an Adaptive Attacker, and Why the Defences That Report Guarantees Use a Gate Instead of a Score

Pranay Mahendrakar 2026

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. Work on trust between language-model agents produces two kinds of object. Protocol work produces identity, attestation, stake and constraint, all bound at the transport layer before any content reaches a model. Behavioural work produces a…

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Journal article

Attribution Is Scored on a Finished Trace: What Failure-Attribution Benchmarks Measure, What Cascade Containment Would Need, and Why Neither Result Bounds the Other

Pranay Mahendrakar 2026

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. A family of recent proposals aims to stop errors from spreading through LLM-based multi-agent systems: genealogy-graph governance over message dependencies, cross-channel causal monitoring, propagation-aware remediation of contaminated state…

Cite Related research All 2 versions: LOR-00001 LOR-00063 · 6 views · 23 downloads Select “Attribution Is Scored on a Finished Trace: What…” for export

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Journal article

Predicting Your Own Failure Is Not Predicting Another Model's Advantage: What Router Results Establish About Agent Self-Knowledge, and the Incremental-Validity Test Nobody Has Run

Pranay Mahendrakar 2026

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. A common reading of the LLM routing literature holds that delegation works without self-knowledge: a router allocates queries using external features of the task and observed outcome statistics over a model pool, and never consults the…

Cite Related research All 2 versions: LOR-00014 LOR-00060 · 6 views · 17 downloads Select “Predicting Your Own Failure Is Not Predicting Another…” for export

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Journal article

A Second Model Is Not a Second Opinion: Peer Verification, Correlated Failure, and the Independence Variable Nobody Reports

Pranay M. Mahendrakar 2026

(c) 2026 Pranay Mahendrakar. Licensed under CC BY 4.0. When a language model cannot reliably detect its own errors, the standard remedy is to have a second model check the first. That substitution is now the default across agent benchmarks, reward pipelines and safety evaluations, and it rests on…

Cite Related research All 2 versions: LOR-00043 LOR-00073 · 12 views · 39 downloads Select “A Second Model Is Not a Second Opinion: Peer Verification…” for export

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