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Notes on robustness
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Game Arena: Strategic LLM Evaluation in Competitive Environments
The technical report describes Kaggle Game Arena, an open platform for head-to-head LLM evaluation in structured competitive environments. It covers pilot environments for chess, poker, and Werewolf, spanning perfect…
NNV3: Expanding Neural Network Verification to New Architectures and Domains
The preprint presents NNV3, a MATLAB framework for formal verification of neural networks. It describes ModelStar for weight perturbation, VolumeStar for video and 3D inputs, GraphStar for graph neural networks…
Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models
The study investigates the performance of object-centric world models for visual model-predictive control, which enable agents to accomplish tasks through planning. The researchers find that these models perform better…
Better Slots, Better Worlds: Representation Quality & Robustness in Object-Centric World Models
The research focuses on object-centric world models (OCWMs) for artificial intelligence agents. The study compares OCWMs with traditional scene-centric models in terms of representation quality, planning success, and…
Retry, Switch, or Abstain? Learning Strategy-Aware Tool-Use Policies via Controlled Error Injection
The paper presents a framework called BENCH2ROBUST, which converts reliable tool-use benchmarks into controlled stochastic environments with failure scenarios. The authors test two interventions to improve the…
Retry, Switch, or Abstain? Learning Strategy-Aware Tool-Use Policies via Controlled Error Injection
Researchers present BENCH2ROBUST, a framework for converting reliable tool-use benchmarks into stochastic environments with controlled solvability. This allows study of agent's response to tool failures, which can be…
V-FiLLM: Verified Financial LLM Reasoning Benchmark
Researchers have created a new framework called V-FiLLM to evaluate the abilities of Language Models (LLMs) in financial reasoning using structured data. The framework generates questions from executable computation…
ContextWeave: A Real-World Workflow Benchmark
The paper introduces ContextWeave, a new benchmark for evaluating the performance of language agents in long-horizon, stateful workflows. The benchmark focuses on memory as an essential component and measures workspace…
Falsifiable Commitment Planning for Self-Correcting Web Agents
Researchers propose a new framework called FCPAgent for web agents to ensure they stay on track during long-term planning. The framework represents each plan step as a Falsifiable Commitment Unit (FCU) that includes…
Agents in the Wild: Where Research Meets Deployment
This tutorial paper discusses the transition of large language model (LLM) based agentic systems from research to practical use in various fields like software engineering, scientific discovery, and finance. It…
AIMO Interpretability Challenge
The text proposes the AIMO Interpretability Challenge, a competition aimed at distinguishing robust from spurious reasoning in advanced mathematical language models. The challenge will provide participants with new…
AIMO Interpretability Challenge
The AIMO Interpretability Challenge is a competition proposed by researchers aimed at distinguishing robust from spurious reasoning in advanced mathematical language models. The challenge focuses on evaluating the…
Traffic-Aware Randomized Smoothing for LLM-Based Network Intrusion Detection
The paper introduces Traffic-Aware Randomized Smoothing (TA-RS), a defense mechanism for Large Language Model (LLM)-based intrusion detection systems to enhance their robustness against feasible traffic manipulation…
ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning
The paper proposes a Q-learning based adaptive retraining approach for Open Radio Access Networks (O-RAN) using Reinforcement Learning (RL). The goal is to balance forecasting accuracy and retraining cost, which can…
Formal Mechanisms for Market Stability in Self-Interested Agent Societies: A Marketplace Simulation Study
This research paper explores mechanisms for maintaining market stability in societies of self-interested agents. The authors conduct a simulation study using DeepSeek-V3 agents and compare eight conditions with…
DT-Guard: Intent-Driven Reasoning-Active Training for Reasoning-Free LLM Safety Guardrail
Researchers present DT-Guard, a content safety guardrail model for large language models in open-world applications. The model uses a Reasoning-Active Training, Reasoning-Free Inference paradigm and aims to improve…
RAISE: LLM-based Automated Heuristic Design with Robust Adversary Instance Search
The paper proposes a framework called Robust Adversary Instance Search (RAISE), which uses Large Language Models (LLMs) to design heuristics that perform well under real-world distributional shifts. The framework…
A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution
Researchers propose a new multi-task malware analysis framework based on Mixture of Experts (MoE) architectures. The system can classify malware families, detect packed versus unpacked malware, and identify malware…
Confidence-Aware Tool Orchestration for Robust Video Understanding
The paper presents a new framework called Robust-TO for video understanding that addresses the issue of 'Blind Trust Problem' in current language models. These models assume every input frame is equally reliable, but…
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