token-efficiency

The paper proposes agentic data cracking, a method to adaptively structure unstructured data during LLM reasoning to reduce token consumption and costs, achieving significant cost cuts while maintaining accuracy on benchmarks.

The tweet highlights how Atomic Agent, a model-agnostic agent layer, improves the performance of GLM 5.3 by executing model actions and preserving state, nearly doubling token usage for only a 77-cent cost increase.

The author measured that 84% of command output in coding agents is unnecessary noise, and shares key principles for filtering it effectively to improve efficiency, such as filtering at execution time and ensuring accurate attribution.

Glean addresses the enterprise AI problem of organizing context by combining company knowledge with models and workflows, reporting 81% lower token costs and 78% preference in benchmarks.

An experiment comparing graph workflows and ReAct-style agent loops for email triage using a local 9B model found that graph workflows significantly reduce token usage with similar accuracy.

The paper introduces Chain-of-Experience, a method for continual improvement of large language models through iterative test-time feedback, demonstrating better performance and cost efficiency across various domains.

The article discusses Qwen 3.8 27B, a 27B parameter model that uses extensive reasoning tokens to compete with larger models, emphasizing trade-offs in token usage and benefits for local deployment.

This paper formalizes and empirically measures whether semantic retrieval via Language Server Protocol saves tokens for coding agents, finding that benefits are conditional and often negative, suggesting an adaptive tool-routing approach.

Caveman is a product on Product Hunt that highlights using fewer tokens, playing on the meme 'why use many token when few do trick.'

IBM Research compares its ALTK-Evolve method to ACE, both agentic-memory systems that let LLM agents learn reusable lessons from past trajectories. ALTK-Evolve delivers individually retrievable guidelines instead of a single evolving playbook, reducing token usage while preserving non-compressed lessons with support counts.

This paper investigates whether pretraining LLMs on artificial languages (pre-pretraining) consistently improves token efficiency across multiple natural languages, finding that gains are highly dependent on experimental setup and random seed, though stable gains appear for small models with the Llama tokenizer.

Nous Research's Hermes introduces Browser Use mode, replacing twelve browser tools with a single script-driven approach using browser_use CLI 3.0, cutting token use by 48-66% with no accuracy drop.

Dan Luu critiques existing benchmarks claiming dynamic languages are more token-efficient for coding agents, arguing that trivial problems and flawed evals make these conclusions unreliable.

Dan Luu critiques claims that dynamic languages are more token-efficient for LLMs, pointing out flaws in existing evals and emphasizing the need for better benchmarking methods.

This paper presents a systematic study of pruning strategies for reducing token usage and latency in long-horizon deep research agents, showing that early pruning yields the largest efficiency gains with little quality loss.

The article introduces Revision Prompting, a technique for industrial LLM processes that improves speed, cost, and consistency when re-processing updated inputs by generating output patches from diffs.

Ix is an open-source tool that maps software architectures into system diagrams, helping AI models reason about codebases more effectively while reducing token usage by 30-99.7%.

A paper presents a structural approach to session compaction, modeling chat history as a DAG with snapshots and branches to trim bloat while preserving meaning, achieving a mean 20% token cut in coding sessions.

A tweet argues that LLM benchmarks should report token usage alongside accuracy, introducing VulcanBench, an open-source benchmark for realistic software engineering tasks that tracks token efficiency and cost.

Google's blog post introduces Agent Skills support in Genkit for Go, demonstrating how progressive disclosure lets agents load specialized knowledge on demand to reduce token usage and improve reliability.