arXiv subjects

Midhun Xavier

Publications and source records attributed to Midhun Xavier.

5 recordsLinked to original sources

arXiv2026

Stabilized Best-of-$K$ Training for Neural Combinatorial Optimization

Leader Reward modifies POMO training to emphasize the best trajectory produced by repeated inference. We test a narrow extension: replace its binary leader/non-leader distinction with a stabilized rank signal indexed by a sampling budget $K$. With the POMO architecture, 3,050-epoch schedule, and TSP-100 test set held fixed, the Leader Reward reimplementation obtains $7.7662$ under 100-start, 8-augmentation greedy decoding, matching the reported $7.766$ at its displayed precision. Under independent sampling, the stabilized $K=8$ recipe lowers realized Best-of-8 cost in all three paired training seeds: $7.7944$ versus $7.8136$. This observation is estimation-only and decoder-specific: three seeds are below the six-seed testing floor, Leader Reward is better at sampled $K=1$, and it remains slightly better under its original augmented-greedy protocol. We make no unbiased-estimator, universal superiority, or state-of-the-art claim.

cs.LG↗

arXiv2026

Rank-Conditioned Sample Reuse for the Plackett--Luce Best-of-$K$ Objective

We study the coupled objective J_K^WOR = E_{S ~ PL-WOR_K}[max_{i in S} R_i]: the expected maximum reward of a size-K Plackett-Luce draw without replacement, the law of Gumbel-Top-K / Stochastic Beam Search decoding. This estimand differs from the conventional i.i.d. objective J_K^iid = E[max_{i<=K} R_i] targeted by existing sample-reuse Max@K estimators, and reusing their i.i.d. weights under the coupled sampler is provably biased (a closed-form three-item instance gives E[g_iid] = (4/5) grad J_K^WOR exactly; pass@K under the coupled sampler is the binary-reward special case). Generic joint-score REINFORCE is already unbiased for J_K^WOR; what it lacks is sample reuse. Our contribution is to instantiate standard rank-conditioned Horvitz-Thompson estimation for the J_K^WOR subset total: from one Gumbel-Top-n pool (n>K) and its observed priority threshold we build an estimator that reuses all C(n,K) embedded K-subsets, unbiased with an unbiased exact score-function surrogate gradient, plus a reward-sorted Max-specific dynamic program that collapses the C(n,K)-term subset sum (with K!-cost set probabilities) exactly to a one-dimensional integral. A fixed-Q quadrature evaluation costs O(n log n + nKQ) arithmetic and is numerically, not algebraically, exact; no epsilon-approximation rate is certified. Each nonzero degree-K Horvitz-Thompson term has finite second moment exactly when n >= 2K; under the same assumptions the full surrogate gradient has finite second moment whenever n >= 2K (sharpness there is open). At K=1 the construction recovers classical priority sampling. All quantities require only the values and differentiable computation graphs of the n+1 drawn items' probabilities, so finite structured sequence policies sampled by exact SBS are covered. A certified finite-Q quadrature bound and countably infinite support remain open. Validation code is included as ancillary files.

cs.LG↗

arXiv2026

IndustriConnect: MCP Adapters and Mock-First Evaluation for AI-Assisted Industrial Operations

AI assistants can decompose multi-step workflows, but they do not natively speak industrial protocols such as Modbus, MQTT/Sparkplug B, or OPC UA, so this paper presents INDUSTRICONNECT, a prototype suite of Model Context Protocol (MCP) adapters that expose industrial operations as schema-discoverable AI tools while preserving protocol-specific connectivity and safety controls; the system uses a common response envelope and a mock-first workflow so adapter behavior can be exercised locally before connecting to plant equipment, and a deterministic benchmark covering normal, fault-injected, stress, and recovery scenarios evaluates the flagship adapters, comprising 870 runs (480 normal, 210 fault-injected, 120 stress, 60 recovery trials) and 2820 tool calls across 7 fault scenarios and 12 stress scenarios, where the normal suite achieved full success, the fault suite confirmed structured error handling with adapter-level uint16 range validation, the stress suite identified concurrency boundaries, and same-session recovery after endpoint restart is demonstrated for all three protocols, with results providing evidence spanning adapter correctness, concurrency behavior, and structured error handling for AI-assisted industrial operations.

cs.SE↗

arXiv2026

Agentproof: Static Verification of Agent Workflow Graphs

Agent frameworks increasingly encode tool-using behavior as explicit workflow graphs, yet safety enforcement remains a runtime concern. These frameworks expose analyzable graph structure through their APIs, enabling pre-deployment static verification of safety properties that runtime guardrails can only check reactively. This paper presents Agentproof, a system that automatically extracts a unified abstract graph model from four major agent frameworks (LangGraph, CrewAI, AutoGen, Google ADK), applies six structural checks with witness trace generation, and evaluates temporal safety policies via a DSL compiled to deterministic finite automata, both statically through a graph x DFA product construction and at runtime over event traces. Unlike general-purpose model checkers, Agentproof requires no manual modeling. In a curated benchmark of 18 author-constructed workflows, 27% of the benchmark contain structural defects (dead-end nodes, unreachable exits) and 55% violate a human-gate policy when enforced, distinct categories that prior work conflates. All 15 temporal policies defined fit within the seven-form DSL fragment, and verification completes in sub-second time for graphs up to 5,000 nodes. The corpus serves as a reproducible benchmark for evaluating static verification tools rather than as a prevalence study; defect rates reflect tool detection capability on a targeted benchmark, not base rates in production systems. Nonetheless, static graph verification complements runtime guardrails by catching topology-level defects that runtime tools miss unless the offending path is exercised.

cs.LO↗

arXiv2022

Plant model generation from event log using ProM for formal verification of CPS

This paper introduces the concept of plant model generation from the recorded traces of events using the process mining technique. The event logs are obtained by visually simulating a simple distributed manufacturing system using the OPC UA communication protocol. The process discovery alpha algorithm is used to extract the process model in Petri net format. The system behavior represented in terms of Petri net is then fed to construct the ECC of the basic function block in compliance with IEC 61499 standard using the proposed notation. Finally, the formal verification of the closed-loop system is done with the help of a tool chain that consists of fb2smv converter, symbolic model checker NuSMV, and other tools for representing counter-examples.

eess.SY↗