Tool use, function calling, orchestration and the protocols that let models act rather than only answer.
arXiv:2608.05446v2 Announce Type: replace Abstract: Long-horizon LLM agents increasingly rely on external execution support to maintain state, track progress, recover from failures, and reuse experie...
arXiv:2609.34054v2 Announce Type: replace Abstract: Multi-LoRA agent systems enable efficient role specialization by sharing a common backbone model. However, each agent repeatedly processes the grow...
arXiv:2610.03945v2 Announce Type: replace Abstract: A constrained optimization problem may involve a parameter in its objective and active constraints, yet the final decision may remain insensitive t...
arXiv:2610.07070v1 Announce Type: new Abstract: Small language models are inexpensive to serve and can run on private infrastructure, but base models are often not good enough at multi-turn tool call...
arXiv:2610.07937v1 Announce Type: new Abstract: Redpine Science gives models and agents a single access point to a wide range of peer-reviewed literature, queried directly through the Model Context P...
arXiv:2610.08630v1 Announce Type: new Abstract: Recently Large Language Models (LLMs) and LLM-based agents increasingly need to incorporate knowledge acquired after pretraining, e.g., domain facts, u...
arXiv:2610.07327v1 Announce Type: cross Abstract: Agent tasks require sequences of interdependent decisions. Autoregressive models support more flexible decision interfaces than conventional classifi...
arXiv:2610.08125v1 Announce Type: cross Abstract: Full-duplex spoken dialogue models listen and speak at the same time, enabling voice agents to have natural, low-latency interactions that turn-based...
arXiv:2604.20658v2 Announce Type: replace Abstract: Multi-agent systems built from teams of large language models (LLMs) are increasingly deployed for collaborative scientific reasoning and problem-s...
arXiv:2610.01108v2 Announce Type: replace Abstract: Retrieval-based speculative decoding (SD) drafts tokens by copying continuations from existing text, which suits coding agents that repeatedly repr...
arXiv:2610.07110v1 Announce Type: new Abstract: High-quality 3D reconstruction of lunar terrain from sparse rover images is indispensable for autonomous lunar exploration, but remains challenging bec...
arXiv:2610.06650v2 Announce Type: replace Abstract: Wikidata is one of the largest open knowledge bases, yet answering a complex question over it still requires a SPARQL query that names the right en...
arXiv:2610.07783v1 Announce Type: new Abstract: Bird's-eye-view (BEV) representations have become a widely used interface between perception and planning in autonomous driving, but they encode what i...
arXiv:2610.07982v1 Announce Type: new Abstract: Monocular metric depth estimation and 3D visual grounding represent the two complementary cornerstones of monocular 3D spatial understanding (M3Sun), f...
arXiv:2610.08713v1 Announce Type: new Abstract: Embodied agents must reason about 3D space while the video is still arriving, answering questions as soon as they have observed enough of the scene. VL...
The study compares two methods for grounding assistants in a small Greek–English agricultural knowledge base: tool‑calling retrieval via a live data interface and vector retrieval‑augmented generation (RAG). Using the KyGround benchmark of 198 questions, vector RAG achieved 95.3% accuracy on canonical Greek questions, outperforming the tool agent’s 71.6% and revealing that the tool agent’s failures stem from literal searches that miss non‑verbatim matches. The results show that search tolerance to user typing variations—such as accents, capitalization, and Greeklish transliterations—is crucial for reliable community knowledge interfaces.
The paper introduces a cost‑aware Hierarchical Multi‑Agent System (HMAS) for ransomware detection and family attribution that prioritizes static analysis and escalates to dynamic and memory analysis only when necessary, thereby reducing analysis time and resource usage. In experiments on 12,439 samples from 16 ransomware families, the deterministic HMAS achieved high F1 scores (0.93) while resolving nearly 58% of cases with static evidence alone and cutting average internal analysis cost by 44.6% compared to exhaustive methods. The system also records a complete provenance trace for each decision and includes optional local LLM review for limited verdict adjustment.
The paper introduces HARP, a framework for orchestrating heterogeneous agents with hidden preferences by maintaining numeric posterior beliefs updated via Bayes’ rule, rather than embedding beliefs in prompts. HARP achieves ∼O(√K) Bayesian regret and, with the HARP+ variant, adds a bonus for informative actions to keep inference active even when optimal actions are uninformative. Experiments across three problem settings show HARP+ outperforms other non‑oracle methods in scenarios where explicit joint inference is infeasible.
The paper evaluates the impact of adding a persistent memory tier to a multi‑agent large language model inference system. While decomposing long‑context inference across agents reduces the peak KV cache usage from about 35 MiB to 14.3 MiB per query, the persistent tier adds only a modest 0.368 MiB to the cache and shows no measurable accuracy improvement across eight dataset pairs. The authors argue that the lack of benefit is structural, as single‑question benchmarks do not provide informative recall opportunities, and they outline conditions and detection procedures for agent‑memory ablations.
RadOnc-Agent is an AI framework that organizes radiotherapy into four clinical phases and offers 26 callable functions via a conversational interface. A large‑language‑model controller maps clinical intent to schema‑constrained calls, maintains patient and workflow context, and routes requests to specialist services. In evaluations, the system achieved high accuracy in single‑function calls (98.79%) and cross‑stage workflow completions (96.50% scripted, 96.67% real‑patient), demonstrating technical feasibility of LLM‑orchestrated coordination across heterogeneous radiotherapy capabilities.