agentic-reasoning
daily-paper-2026-09-11-reasoning-verbosity-tax-agentic-cost
The Reasoning Verbosity Tax: Per-Turn Cost-Quality Frontiers of Explicit vs. Compact Chain-of-Thought in Self-Hosted Agentic LLMs on H200
TL;DR — An analytical paper that turns the Qwen3 thinking toggle into a priced dial for self-hosted agentic LLM loops: explicit chain-of-thought persisted in context compounds a per-turn verbosity tax quadratically in the horizon (Theta(T^2)) versus linearly (Theta(T)) under a discard policy, the relative tax widens monotonically toward a… See the full description on the dataset page: https://huggingface.co/datasets/thaki-AI/daily-paper-2026-09-11-reasoning-verbosity-tax-agentic-cost.agentic-synth-reasoning
Agentic Synth Reasoning
Synthetic reasoning traces for multi-step agentic behavior, generated incrementally from nvidia/Nemotron-SFT-Agentic-v2.
Dataset Summary
Each record contains a multi-turn agentic conversation with synthetically generated reasoning traces (reasoning_content) attached to each assistant turn. The reasoning was produced incrementally — for each assistant message, only the conversation prefix up to that point was fed to the reasoning model… See the full description on the dataset page: https://huggingface.co/datasets/mkurman/agentic-synth-reasoning.json-mode-agentic-reasoningagentic-reasoning-benchmark
Agentic & Reasoning Benchmark (ARB) – Expanded
Ein synthetischer Benchmark mit 2.550 Fragen und Lösungen, optimiert für die Evaluation von Agentic Capabilities und Reasoning.
Überblick
Eigenschaft
Wert
Anzahl Beispiele
2.550
Kategorien
8
Schwierigkeitsgrade
easy / medium / hard
Formate
CSV + JSON
Reproduzierbarkeit
Generator-Skript (seed=42) enthalten
Lizenz
CC-BY-4.0
Kategorien
Kategorie
Anzahl
Beschreibung… See the full description on the dataset page: https://huggingface.co/datasets/roskosmos19/agentic-reasoning-benchmark.agentic-reasoning-trace-summaries-40k
Reasoning Summary JSON Dataset
We built this dataset to train models to turn long reasoning/work traces into short structured summaries.
Each example has a verbose trace in input and a compact JSON summary in output. The summary is shaped like the kind of progress update we want a model to produce while it is working: a title, a subtitle, a short summary, and the current task.
The dataset is JSONL with 43,734 rows. The rows are ordered from longest to shortest so long-context… See the full description on the dataset page: https://huggingface.co/datasets/Qyrou/agentic-reasoning-trace-summaries-40k.tool-reasoning-sft-CODING-nvidia-Nemotron-Agentic-v1
Nemotron-Agentic-v1 — Cleaned & Rectified
335k multi-turn agentic tool-use trajectories from NVIDIA's Nemotron-Agentic-v1, converted into a strict reasoning + tool-call format with validated FSM transitions.
Origin
Derived from nvidia/Nemotron-Agentic-v1.
Nemotron-Agentic-v1 is a synthetic dataset of multi-turn conversations where language models decompose user goals, decide when to call tools, and reason over tool outputs. Trajectories are generated by simulating user… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-CODING-nvidia-Nemotron-Agentic-v1.