arXiv:2608. 12123v1 Announce Type: cross Abstract: LLM-agent services repeatedly execute small deterministic transitions between model and tool calls: route an outcome, update state, and emit the next effect.
arXiv:2606. 19989v1 Announce Type: cross Abstract: Modern LLM training breaks a core assumption behind offline batch samplers: the true training cost of a sample is only observable after preprocessing, augmentation, templating, tokenization, and multimodal visual-token expansion.
arXiv:2606. 09682v1 Announce Type: new Abstract: AutoMegaKernel (AMK) compiles a HuggingFace Llama-family model into a single persistent cooperative CUDA kernel that runs the whole forward pass in one launch, with no per-model hand-written CUDA.
arXiv:2608. 03682v1 Announce Type: new Abstract: Physical AI policies require inference throughout their lifecycle, including model evaluation, cloud reinforcement learning rollout, edge GPU serving, and onboard deployment.
arXiv:2607. 00297v1 Announce Type: new Abstract: When LLM agents use evaluator feedback to adapt their behavior in closed loops, evaluator biases propagate through the agent's strategy distribution -- a phenomenon known as evaluator preference coupling.
arXiv:2610.22769v1 Announce Type: cross Abstract: The companion MPI-extension paper validated the first communication-bearing machine-shape ensemble: four NVIDIA A100 ranks and four CPU ranks on one...
arXiv:2608. 02630v1 Announce Type: new Abstract: Knowledge graph engineering often distributes accepted state, observations, constraints, processes, and hypothetical scenarios across artifacts whose combined execution contract remains external.
The paper reports the first empirical, phase‑decomposed benchmark of handshake overhead in three major AI agent communication protocols—MCP (2025 and 2026 versions), A2A, and ACP—using their official SDKs. It measures round‑trip counts, per‑phase latency, wire bytes, and token cost across cold‑start, warm‑repeat, capability‑mismatch, and degraded‑metadata scenarios, finding that round‑trip count dominates cost (e.g., MCP’s legacy handshake requires three round trips and 230 ms at 50 ms RTT, while A2A needs one round trip and 61 ms). The study also shows that handshake cost scales with the counterpart’s capability inventory, with a 200‑capability counterpart consuming 11 % of a 200k‑token context window, and documents protocol evolution that reduces MCP’s handshake from three to zero round trips on warm reconnects.
KOSMOS is a library that enables self‑scheduling persistent kernels for transformer training, allowing workgroups to switch between communication and computation roles as data becomes ready without a scheduler or tuned split. It fuses all tensor and sequence parallelism collectives with GEMM operations and runs entire expert layers in a single launch per direction. On AMD Instinct GPUs, KOSMOS improves TP+SP end‑to‑end training in Megatron‑LM by up to 1.28× and accelerates DeepSeek‑V3 and Qwen3‑235B proxies by 1.29–1.83× over DeepEP.
The paper introduces NOSTRAdAMUS, a predictive link‑adaptation framework for 5G NR that forecasts retransmissions in the next radio frame using recent HARQ history and adjusts the Modulation and Coding Scheme accordingly. Gradient Boosting models achieve 82.9% overall accuracy, with high‑confidence predictions correct 94.2% of the time and a 5.5 µs inference latency. Evaluated OTA on the X5G testbed and various channel emulators, the approach boosts goodput by up to 71.5% and cuts retransmissions by up to 71.8% without retraining across diverse scenarios.
arXiv:2608. 02680v1 Announce Type: cross Abstract: Tool-using language-model agents repeatedly rediscover procedures they have already executed, producing traces that mix reusable structure with retries, exploration, accidental ordering, and repeated lookups.
arXiv:2607. 20488v1 Announce Type: new Abstract: Multi-agent LLM frameworks typically fix their team topology at boot time.