Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
arXiv:2610.07335v1 Announce Type: cross Abstract: Improving the reliability of large language model (LLM) agents in long-horizon decision-making remains a key challenge. When deployed as autonomous a...
arXiv:2610.07339v1 Announce Type: cross Abstract: Tactical Combat Casualty Care (TC3) requires responders to connect visual observations of injuries and interventions with established clinical guidan...
The paper introduces SEMAADB, a dataset comprising 3,000 engineering contexts and 15,000 SysML diagrams, each context containing five interconnected views (Requirement, Block Definition, Activity, State Machine, and Sequence). The authors verified diagram consistency and created a 100-context human‑verified benchmark. They evaluated three language models on diagram repair and cross‑diagram update tasks, finding that while syntax repair is largely solved, semantic repair and cross‑diagram consistency remain challenging.
arXiv:2610.07457v1 Announce Type: cross Abstract: Fine-grained mixed-precision quantization promises efficient large language model inference, but local precision choices can conflict with regular GP...
arXiv:2610.07460v1 Announce Type: cross Abstract: Inserting objects into existing 3D scenes requires more than selecting a plausible location: the inserted object must also fit local geometry while...
arXiv:2610.07532v1 Announce Type: cross Abstract: LLMs have advanced rapidly, raising growing concerns about their safety. Recent work has proposed approaches to detect and defend against attacks inc...
arXiv:2610.07535v1 Announce Type: cross Abstract: Multi-agent simulations with large language models (LLMs) often operate networks of agents with a single base model. This overlooks the inter-model e...
The paper introduces Physics‑Guided Visual Prompting (PG‑VP), a plug‑and‑play module that overlays a virtual obstacle onto the input of a frozen Vision‑Language‑Action model to guide navigation around invisible hazards such as radiation or temperature spikes. PG‑VP performs a physics‑based risk assessment to determine the avoidance direction and dynamically renders the same virtual obstacle across frames, allowing the existing navigation policy to detour without retraining. Experiments on OmniNav with R2R‑CE and RxR‑CE datasets show that PG‑VP steers the policy toward low‑risk actions in 84.9% and 83.2% of cases, while real‑world tests on a robot demonstrate significant safety improvements against thermal and radiation sources.
arXiv:2610.07607v1 Announce Type: cross Abstract: Model-guided directed evolution seeks to identify high-fitness protein variants under limited oracle budgets. Protein language models (PLMs) provide...
arXiv:2610.07625v1 Announce Type: cross Abstract: Automated research systems increasingly run LLM agents over long horizons, but more inference does not by itself produce more progress: agents replay...
arXiv:2610.07643v1 Announce Type: cross Abstract: Most KV-cache eviction methods ask, in effect, which memory appeared important while reading the prompt? We instead ask, which memory will matter whi...
arXiv:2610.07654v1 Announce Type: cross Abstract: On-policy distillation (OPD) has attracted growing attention as an effective way to transfer capabilities from teacher models to student models. Rece...
CACHEFORGE introduces a novel framework that uses a large language model (LLM) to evolve cache‑replacement policies end‑to‑end. In each iteration, the LLM generates new C++ replacement logic, which is evaluated by a trace‑based simulator and refined through reward shaping, structural checks, and mutation. The resulting policies are compact, hardware‑aware, and outperform existing CRC‑2 baselines on SPEC CPU2006, achieving significant improvements in hit rate and IPC across diverse workloads.
arXiv:2610.07676v1 Announce Type: cross Abstract: Research on transformer expressivity shows whether a transformer is capable of solving a given task, but gives little indication of whether the solut...
arXiv:2610.07730v1 Announce Type: cross Abstract: Typed decision models answer a declared question without generating text: a decision head returns a probability for each of the declared options in a...
arXiv:2610.07742v1 Announce Type: cross Abstract: Optimized kernels such as FlashAttention and FlashDecoding are crucial for accelerating today's large models. Most of them are handwritten by experts...
arXiv:2610.07817v1 Announce Type: cross Abstract: Organizations automating operational processes need more than a correct outcome: they need to predict how a process will run, know which one actually...
arXiv:2610.07832v1 Announce Type: cross Abstract: Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks. However, e...
arXiv:2610.07863v1 Announce Type: cross Abstract: Long-horizon LLM agents act on an append-only interaction history that is re-sent to the model at every step, so the context and its cost grow with s...
arXiv:2610.07913v1 Announce Type: cross Abstract: Gastric adenocarcinoma (GA) is a leading cause of cancer-related mortality worldwide, and accurate histopathological subtype classification from whol...