arXiv Machine Learning
Jul 13
Mach-Mind-4-Flash Technical Report
arXiv:2607. 09375v1 Announce Type: new Abstract: We present Mach-Mind-4-Flash, a 35B-parameter Mixture-of-Experts (MoE) agentic model with 3B activated parameters.
Agent-Conditioned Routing: Diverse Reasoning Paths Within One Frozen Model
Agent-Conditioned Routing (ACR) is a technique that enables a frozen sparse mixture-of-experts model to generate diverse reasoning paths from a single prompt without fine‑tuning or user‑assigned roles. By assigning persistent, untrained expert‑eligibility masks while keeping the model’s token router intact, ACR produces more problems with at least two distinct primary methods compared to temperature sampling on GLM 5.3 and GPT‑5.6 Sol. ACR adds minimal overhead (≈160 KiB) and maintains latency and VRAM usage within 3% of standard sampling.
Related stories
arXiv Computation and Language
Sep 14
Expert-Space Exploration in MoE Reinforcement Learning
arXiv:2609.13058v1 Announce Type: new Abstract: Reinforcement learning (RL) has become central to post-training of large language models. Recent advances in RL for Mixture-of-Experts (MoE) models hav...
arXiv Machine Learning
Jul 31
Beyond Geometric Complementarity: Coherent Overlap in Sparse Mixture-of-Experts Routing
arXiv:2607. 28308v1 Announce Type: new Abstract: Sparse mixture-of-experts (MoE) language models route each token to multiple experts, suggesting a geometric account of their benefit: co-selected experts should contribute distinct representation directions.
Hugging Face Trending Papers
Jul 30
Beyond Geometric Complementarity: Coherent Overlap in Sparse Mixture-of-Experts Routing
Sparse mixture-of-experts (MoE) language models route each token to multiple experts, suggesting a geometric account of their benefit: co-selected experts should contribute distinct representation directions. Existing evidence often conflates route coherence, candidate quality, and candidate-by-context interaction.
arXiv AI
Sep 15
The Router Within: Eliciting Native Skill Routing from a Frozen LLM
arXiv:2609.15982v1 Announce Type: cross Abstract: Skills extend an LLM agent beyond its parametric knowledge, and the gain they promise rests on picking the right one. Deployed harnesses route by pre...
arXiv AI
Jun 15
Token-Level LLM Collaboration via FusionRoute
arXiv:2601. 05106v5 Announce Type: replace Abstract: Large language models (LLMs) exhibit strengths across diverse domains.
arXiv AI
1d ago
Rules, Traces and Routing Statistics: Training and Calibration Data for Open Models on ARC-AGI-2
The paper reports on training and calibration experiments for three open models on the ARC‑AGI‑2 benchmark, using offline runs on four L4 GPUs for up to 12 hours. For the NVARC Qwen3‑4B grid model, a curated corpus of 12,000 executable rules and 95,993 tasks was built, and fine‑tuning on 1,920 real task records raised the semi‑private score from 28.96 to 33.06, with no synthetic‑task checkpoint outperforming the real‑task model. For the Qwen3.8‑27B reasoning model, fine‑tuning on 1,036 of its own reasoning traces and a specialized inference procedure increased the score from 26.81 to 44.03, while the Qwen3.8‑Flash‑Next mixture‑of‑experts model achieved a best score of 56.67 after expert pruning, routing statistics adaptation, and an FP8 indexer cache.
arXiv AI
Aug 21
Learning When to Think: Adaptive Reasoning for Test-Time Compute Allocation
arXiv:2608. 20256v1 Announce Type: new Abstract: Reasoning language models trained with reinforcement learning typically operate under a fixed token budget rather than an explicitly adaptive one, which can lead to over-computation on easy problems and insufficient computation on difficult ones.
arXiv Machine Learning
Aug 27
Ban&Pick: Enhancing Performance and Efficiency of MoE-LLMs via Smarter Routing
The paper introduces Ban&Pick, a post‑training, plug‑and‑play routing strategy for Sparse Mixture‑of‑Experts large language models. It identifies and reinforces a small group of highly influential experts while dynamically pruning redundant ones, leading to accuracy gains across math, code, and reasoning benchmarks. Experiments on DeepSeek and Qwen3 show notable performance improvements and a 1.25× inference speedup without retraining or architectural changes.
arXiv AI
Jul 14
Route, Communicate, and Reason: Gated Routing and Adaptive Depth for Efficient Multi-Agent Reasoning
arXiv:2607. 10836v1 Announce Type: new Abstract: Multi-agent ensembling multiplies active parameters and inference cost without answering three basic questions: which agents to consult, how deeply a query should traverse a hierarchy of agents, and when inter-agent communication is worth its cost.
arXiv AI
1d ago
Routes of Reasoning: Reading and Reshaping Test-Time Compute in MoE Models
The paper introduces a method to control reasoning behaviors in Mixture-of-Experts (MoE) language models by labeling sentence-level segments with six distinct reasoning behaviors (planning, execution, restatement, fact, reflection, and conclusion). Linear probes on MoE router logits can predict these behaviors nearly as accurately as probes on the full hidden state, enabling the authors to edit router scores during inference to reduce overthinking. This intervention cuts generated tokens by a median of 30.4% (up to 63.3%) across multiple models and benchmarks without significant loss in accuracy, and often improves it.
arXiv AI
Aug 18
LLMs Can Predict Failure Risk, But Struggle to Predict Which Collaboration Protocol Pays Off: Cost-Aware Protocol Routing Across Reasoning Tasks
arXiv:2608. 14927v1 Announce Type: new Abstract: Multi-agent large language model (LLM) systems can improve reasoning by spending more computation, but deployment requires deciding when extra collaboration is worth its cost.