arXiv AI

frb100-40 After Two Decades: An Optimality Certificate and a Preregistered Search Study

The paper presents a verified solution to the long‑standing frb100‑40 benchmark, providing a 100‑vertex independent set and a partition into 100 cliques of size 40, thereby proving the maximum independent‑set size is 100 and the minimum vertex‑cover size is 3,900. A preregistered experimental campaign of 8,668 runs evaluated new repair operators, finding no performance improvement over the baseline ULSA algorithm. Additional comparisons with other solvers (LibMVC‑NuMVC, group‑aware CSP pipeline) and exhaustive enumeration confirm the optimality certificate and characterize the search barrier for this instance.

arXiv AI
3d ago

Who Verifies the Graph? Misspecification Attacks on Causal Action Verification for Language Agents

The paper investigates how causal action verifiers, which guard language agents’ tool calls by checking identifiability against a committed action‑state graph, can be compromised through small graph misspecifications. By removing a single bidirected edge or reversing an arrowhead, the authors demonstrate that a verifier (CIVeX) that originally had zero false executions can suffer false execution rates up to 48.9%, with most of those executions being harmful and overall utility dropping dramatically. An additional attestation step that samples executions can detect these attacks with few false alarms, but it also leads to many wrongful rejections that reduce beneficial actions and incur significant experimental costs. whyItMatters":"The study shows that even minor errors in the verifier’s underlying graph can drastically undermine safety and performance, highlighting the need for robust auditing mechanisms."

By Fabio Rovai
arXiv Machine Learning
Sep 2

Deterministic LLM Inference Across GPU Kernels: Power-of-Two INT8 Quantization Scales and the Limits of Tolerance-Based Conformance

The paper evaluates the effectiveness of tolerance‑based conformance tests for INT8 quantized GEMM kernels used in large language models. By injecting nine faults into a Qwen3‑1.7B reference pipeline, the authors show that most faults shift outputs by at most one bfloat16 spacing, rendering a tolerance of one spacing blind to these errors. They further demonstrate that requantizing weight scales to the nearest power of two aligns CUTLASS and Triton implementations bit‑for‑bit and produces identical token sequences, with only minor perplexity changes.

By Teng-Ruei Chen
arXiv Machine Learning
Jul 30

HoF-Bench: Rediscovering Real AI-Discovered CVEs Without Frontier Models

arXiv:2607. 27030v1 Announce Type: cross Abstract: LLM-based analyzers have begun finding real vulnerabilities in mature open-source projects: AISLE's analyzer is credited with more than 280 CVEs across 78 projects, including OpenSSL, curl, and GnuTLS.

By Petr Simecek, Elnaz Babayeva, Jiri Balhar, Michal Bida, Michal Buran, Vaclav Cadek, Luigino Camastra, Tomas Dulka, Michal Janocko, Tomas Klohna, Pavel Kohout, Ondrej Kokes, Adam Krivka, Jakub Kubik, Patrik Mada, Igor Morgenstern, Marek Pavelka, Joshua Rogers, Petr Stastny, Jan Tattermusch, Dmitrijs Trizna, Martin Votruba, Guido Vranken, Jakub Zikl, Evelina Gabasova, Stanislav Fort
arXiv Machine Learning
Sep 24

Does Graph Structure Earn Its Place in Microservice Root-Cause Analysis? A Controlled Study on RCAEval, and What the Benchmark Was Really Measuring

The paper investigates whether graph structure improves microservice root‑cause analysis by conducting a controlled study on the RCAEval benchmark. Using identical features, optimizers, and evaluation protocols across three model variants, the authors find no consistent advantage for graph‑based models over flat models, with a negligible Avg@5 difference (0.003, p=0.844). They identify two benchmark properties—limited fault injection and a non‑uniform telemetry schema—that bias results, and propose a new model, PSC‑GRCA, which achieves higher Avg@5 mainly through a system prior rather than graph information.

By Imad Bulji\'c
arXiv Machine Learning
Aug 31

CURA: Certified Runtime Alarms for Computer-Use Agents

arXiv:2608.27808v1 Announce Type: cross Abstract: Self-report is the cheapest oversight channel a deployer has, and on capable computer-use agents (CUAs) it fails precisely where oversight matters. O...

By Divake Kumar, Sina Tayebati, Devashri Naik, Amanda Sofie Rios, Nilesh Ahuja, Omesh Tickoo, Ranganath Krishnan, Amit Ranjan Trivedi