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Notes on llm
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Pre-Compiled Pipeline Shards for Distributed LLM Inference on Intel AI PC Fleets
The authors present a method to distribute Large Language Model (LLM) inference across multiple Intel AI PCs using pre-compiled pipeline shards. This allows for efficient use of idle AI PCs with integrated GPUs and…
NetlistBench: Evaluating LLM Reliability in SPICE Netlist Recognition and Manipulation
Researchers present NetlistBench, a new benchmark for evaluating the reliability of Large Language Models (LLMs) in SPICE netlist recognition and manipulation tasks. The study finds that while LLMs perform well on…
HarnessOpt-Bench: Evaluating LLMs at Harness Optimization
Researchers introduce HarnessOpt-Bench, a benchmark for evaluating the performance of Large Language Models (LLMs) in optimizing their own harnesses. The study examines five frontier LLMs on four tasks and finds that…
From Textual Requirements to Microservice Architectures - A Comprehensive Evaluation of LLM-Based Design Synthesis
Researchers evaluated the ability of a Large Language Model (OpenAI o3) to generate microservice architectures based on textual requirements. The study found that the LLM was more effective when given examples…
OmegaUse-OfficeVal: Benchmarking LLM Agents on Long-Horizon Office-Suite Tasks with Economic Grounding
Researchers have developed a new benchmark called OmegaUse-OfficeVal to test the performance of large language model (LLM) agents in completing long-horizon office-suite tasks. The benchmark includes 100 tasks that…
Gubernaut: A Deterministic Homeostatic Controller for Affect-Regulated LLM Agents, Validated Across Independent Model Families
The research presents a new model-agnostic controller, Gubernaut Cognitive Controller (GCC), designed to control the behavior of Large Language Model (LLM) agents. The GCC aims to mitigate reactive failure modes in…
Simulating Tenant Responses to Energy Policy Interventions with Transaction-Cost-Aware LLM Age
Researchers developed a method to simulate how tenants respond to energy-efficient renovation policies using large language models (LLMs). They introduce a friction-aware persona modeling approach that takes into…
Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog
The paper introduces Euclid-MCP, an open-source MCP server that provides deterministic logical reasoning via SWI-Prolog. It aims to bridge the gap between Large Language Models (LLMs) and multi-step logical reasoning…
Towards Agentic Agent-based Models: Feasibility, Performance, and Statistical Model Checking
Researchers explore the feasibility, performance, and reliability of using large language models (LLMs) in Agent-based Models (ABMs), specifically with Mesa ABM models. They introduce a hybrid population to test…
Experience Memory Graph: One-Shot Error Correction for Agents
Researchers propose a new framework called Experience Memory Graph (EMG) to help large language model agents recover from failures more effectively in complex, long-horizon tasks. Instead of using prompt-based…
Experience Memory Graph: One-Shot Error Correction for Agents
The researchers propose a new framework called Experience Memory Graph (EMG) to help large language model agents recover from failures more effectively in complex, long-horizon tasks. EMG reformulates failure recovery…
Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias
The research investigates bias in Large Language Models (LLMs) acting as judges by exploring representation-level accounts, focusing on the hidden state of the LLM, complementing input-output level studies. The study…
Reproducing human biases in route choice using large language models: Toward scalable behavioral modeling
This research explores the ability of large language models (LLMs) to reproduce human biases in route choice decisions, traditionally described using Cumulative Prospect Theory (CPT). The study finds that LLMs can…
SolarChain-Eval: A Physics-Constrained Benchmark for Trustworthy Economic Agents in Decentralized Energy Markets
The authors propose SolarChain-Eval, a benchmark for evaluating the trustworthiness of economic agents in decentralized energy markets. This benchmark ensures that agents make hourly decisions based on a…
Workflow as Knowledge: Semantic Persistence for LLM-Mediated Workflows
The paper proposes a conceptual model for large language model (LLM) applications that use explicit workflows. The model is designed to represent various elements of the workflow such as definitions, instances, and…
AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents
The authors introduce a new bounded contract for long-horizon Large Language Model (LLM) agents in the game Slay the Spire 2. The contract allows decisions to be made from a fresh user message, with no raw…
LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning
The researchers introduce a new testbed called LACUNA for evaluating the precision of post hoc removal methods (unlearning) for LLMs. The testbed focuses on injecting personal identifiable information into specific…
Online Safety Monitoring for LLMs
Researchers propose a simple online safety monitoring system for Large Language Models (LLMs). The system uses a verifier signal from an external model to decide when safety can no longer be assumed, by setting a…
LLM-Guided ODE Discovery and Parameter Inference from Small-Cohort Aggregate Data
A new method called AgentODE has been proposed to discover and refine Ordinary Differential Equations (ODE) structures for modeling complex dynamics, particularly useful in clinical settings. This method works with…
A Tutorial on Autonomous Fault-Tolerant Control Using Knowledge-Grounded LLM Agents
This research paper proposes a framework for using Large Language Model (LLM) agents to support operators in recovering from faults in process plants. The LLM is used as a constrained supervisory planner, proposing…
ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping
The paper proposes ShopX, a foundation model aimed at bridging the gap between language understanding and item-space fulfillment in AI shopping applications. It unifies intent understanding, execution planning, and…
OpenRCA 2.0: From Outcome Labels to Causal Process Supervision
Researchers introduce a new method, called PAVE, to support the rigorous evaluation of large language models (LLMs) in root cause analysis (RCA). The current datasets only label the root cause and not the path…
GUI agent: Guided Exploration of User-Sensitive Screens
The text discusses a paper that proposes a solution for a problem in Large Language Model (LLM) agents used within Graphical User Interface (GUI) environments. The current issue is that these agents, fine-tuned to…
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