Resilient by design: The agentic supply chain - Deloitte
<a href="https://news.google.com/rss/articles/CBMi3wFBVV95cUxPSDJ6MnZWMS1WVmZleXE2bHFrS0NzczJ4ajVXQl9KQ0Y1X2xvZnYydEtFajBkZ2VRbzlTa01CSWxCbExNV1pNODhWTzhzdllsTFdRWWtzV2FsdU1uM0hObjJXZkRGSmZ1djJFZWlpZHlBQnZGdWNuRWZPeld2YVlJYzNJQWxhSlk3T0V2UXVTUXBfcUJHZFNKekFyYTYwbndlcFBvR1JIMnpudjlhWjJLVC1GZGE0a0hUNno4ZjQ5Qlp1LXRXTjk2SkgwRTV1YzB1cFhyRFM1V0hRN1ZNWnpF?oc=5" target="_blank">Resilient by design: The agentic supply chain</a> <font color="#6f6f6f">Deloitte</font>
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Execution-Verified Reinforcement Learning for Optimization Modeling
arXiv:2604.00442v1 Announce Type: new Abstract: Automating optimization modeling with LLMs is a promising path toward scalable decision intelligence, but existing approaches either rely on agentic pipelines built on closed-source LLMs with high inference latency, or fine-tune smaller LLMs using costly process supervision that often overfits to a single solver API. Inspired by reinforcement learning with verifiable rewards, we propose Execution-Verified Optimization Modeling (EVOM), an execution-verified learning framework that treats a mathematical programming solver as a deterministic, interactive verifier. Given a natural-language problem and a target solver, EVOM generates solver-specific code, executes it in a sandboxed harness, and converts execution outcomes into scalar rewards, opti
In harmony with gpt-oss
arXiv:2604.00362v1 Announce Type: new Abstract: No one has independently reproduced OpenAI's published scores for gpt-oss-20b with tools, because the original paper discloses neither the tools nor the agent harness. We reverse-engineered the model's in-distribution tools: when prompted without tool definitions, gpt-oss still calls tools from its training distribution with high statistical confidence -- a strong prior, not a hallucination. We then built a native harmony agent harness (https://github.com/borislavmavrin/harmonyagent.git) that encodes messages in the model's native format, bypassing the lossy Chat Completions conversion. Together, these yield the first independent reproduction of OpenAI's published scores: 60.4% on SWE Verified HIGH (published 60.7%), 53.3% MEDIUM (53.2%), and
Improvisational Games as a Benchmark for Social Intelligence of AI Agents: The Case of Connections
arXiv:2604.00284v1 Announce Type: new Abstract: We formally introduce a improvisational wordplay game called Connections to explore reasoning capabilities of AI agents. Playing Connections combines skills in knowledge retrieval, summarization and awareness of cognitive states of other agents. We show how the game serves as a good benchmark for social intelligence abilities of language model based agents that go beyond the agents' own memory and deductive reasoning and also involve gauging the understanding capabilities of other agents. Finally, we show how through communication with other agents in a constrained environment, AI agents must demonstrate social awareness and intelligence in games involving collaboration.
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