EffGen: Enabling Small Language Models as Capable Autonomous Agents

• URL: http://arxiv.org/abs/2602.00887v1
• Date: Sat, 31 Jan 2026 20:24:56 GMT
• Title: EffGen: Enabling Small Language Models as Capable Autonomous Agents
• Abstract summary: effGen is an agentic framework optimized for small language models (SLMs)<n>It makes four major contributions: (1) Enhanced tool-calling with prompt optimization that compresses contexts by 70-80%; (2) Intelligent task decomposition that breaks complex queries into parallel or sequential subtasks based on dependencies; and (3) Unified memory system combining short-term, long-term, and vector-based storage.
• Score: 21.17719258111762
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most existing language model agentic systems today are built and optimized for large language models (e.g., GPT, Claude, Gemini) via API calls. While powerful, this approach faces several limitations including high token costs and privacy concerns for sensitive applications. We introduce effGen, an open-source agentic framework optimized for small language models (SLMs) that enables effective, efficient, and secure local deployment (pip install effgen). effGen makes four major contributions: (1) Enhanced tool-calling with prompt optimization that compresses contexts by 70-80% while preserving task semantics, (2) Intelligent task decomposition that breaks complex queries into parallel or sequential subtasks based on dependencies, (3) Complexity-based routing using five factors to make smart pre-execution decisions, and (4) Unified memory system combining short-term, long-term, and vector-based storage. Additionally, effGen unifies multiple agent protocols (MCP, A2A, ACP) for cross-protocol communication. Results on 13 benchmarks show effGen outperforms LangChain, AutoGen, and Smolagents with higher success rates, faster execution, and lower memory. Our results reveal that prompt optimization and complexity routing have complementary scaling behavior: optimization benefits SLMs more (11.2% gain at 1.5B vs 2.4% at 32B), while routing benefits large models more (3.6% at 1.5B vs 7.9% at 32B), providing consistent gains across all scales when combined. effGen (https://effgen.org/) is released under the MIT License, ensuring broad accessibility for research and commercial use. Our framework code is publicly available at https://github.com/ctrl-gaurav/effGen.

Related papers

• Kernel-Managed Shared Memory for System-Wide Personalization [36.23690583022494]
  kernel-managed shared memory is a system-level abstraction in which specialized agents write structured, tagged memories.<n>We implement and evaluate this design on AIOS and compare it against three alternatives across three assistant models.<n>Results indicate that centralizing memory management in the agent-system kernel delivers most of the personalization benefit of unconstrained context at a fraction of its cost.
  arXiv  Detail & Related papers  (2026-09-09T13:20:47Z)
• optimize_anything: A Universal API for Optimizing any Text Parameter [98.42497715725356]
  We show a single AI-based optimization system-supporting single-task search, multi-task search with cross-problem transfer, and generalization to unseen inputs.<n>We show for the first time that text optimization with LLM-based search is a general-purpose problem-solving paradigm.
  arXiv  Detail & Related papers  (2026-05-19T10:18:12Z)
• MemMachine: A Ground-Truth-Preserving Memory System for Personalized AI Agents [2.541923091180284]
  Large Language Model (LLM) agents require persistent memory to maintain personalization, factual continuity, and long-horizon reasoning.<n>We present MemMachine, an open-source memory system that integrates short-term, long-term episodic, and profile memory.<n>MemMachine uses contextualized retrieval that expands nucleus matches with surrounding context, improving recall when relevant evidence spans multiple dialogue turns.
  arXiv  Detail & Related papers  (2026-04-06T16:57:06Z)
• AWE: Adaptive Agents for Dynamic Web Penetration Testing [0.0]
  AWE is a memory-augmented multi-agent framework for autonomous web penetration testing.<n>It embeds structured, vulnerability-specific analysis pipelines within a lightweight LLM orchestration layer.<n>AWE achieves substantial gains on injection-class vulnerabilities.
  arXiv  Detail & Related papers  (2026-03-01T07:32:42Z)
• AgentSM: Semantic Memory for Agentic Text-to-SQL [16.68006461152765]
  Agent Semantic Memory (AgentSM) builds and leverages interpretable semantic memory.<n>AgentSM reduces average token usage and trajectory length by 25% and 35%, respectively, on the Spider 2.0 benchmark.<n>It also improves execution accuracy, reaching a state-of-the-art accuracy of 44.8% on the Spider 2.0 Lite benchmark.
  arXiv  Detail & Related papers  (2026-01-22T07:31:19Z)
• SCOPE: Prompt Evolution for Enhancing Agent Effectiveness [53.75986399936395]
  Large Language Model (LLM) agents are increasingly deployed in environments that generate massive, dynamic contexts.<n>While agents have access to this context, their static prompts lack the mechanisms to manage it effectively.<n>We introduce textbfSCOPE (Self-evolving Context Optimization via Prompt Evolution)<n>We propose a Dual-Stream mechanism that balances tactical specificity (resolving immediate errors) with strategic generality (evolving long-term principles)
  arXiv  Detail & Related papers  (2025-12-17T12:25:05Z)
• MemSearcher: Training LLMs to Reason, Search and Manage Memory via End-to-End Reinforcement Learning [73.27233666920618]
  We propose MemSearcher, an agent workflow that iteratively maintains a compact memory and combines the current turn with it.<n>At each turn, MemSearcher fuses the user's question with the memory to generate reasoning traces, perform search actions, and update memory to retain only information essential for solving the task.<n>We introduce multi-context GRPO, an end-to-end RL framework that jointly optimize reasoning, search strategies, and memory management of MemSearcher Agents.
  arXiv  Detail & Related papers  (2025-11-04T18:27:39Z)
• UltraCUA: A Foundation Model for Computer Use Agents with Hybrid Action [77.63125913907771]
  We present UltraCUA, a foundation model that bridges the gap between GUI primitives and high-level programmatic tool calls.<n>Experiments with our 7B and 32B models demonstrate substantial improvements over state-of-the-art agents.
  arXiv  Detail & Related papers  (2025-10-20T17:48:26Z)
• dInfer: An Efficient Inference Framework for Diffusion Language Models [54.80918957287927]
  Diffusion-based large language models (dLLMs) have emerged as a promising alternative to autoregressive (AR) LLMs.<n>We present dInfer, an efficient and framework for dLLM inference.
  arXiv  Detail & Related papers  (2025-10-09T16:19:42Z)
• Graph of Agents: Principled Long Context Modeling by Emergent Multi-Agent Collaboration [9.151759069858924]
  We introduce a principled framework that formalizes the model-agnostic long context modeling problem as a compression problem.<n>We propose Graph of Agents (GoA), which dynamically constructs an input-dependent collaboration structure that maximizes this objective.<n>GoA surpasses the 128K context window Llama 3.1 8B on LongBench, showing a dramatic increase in effective context length.
  arXiv  Detail & Related papers  (2025-09-26T04:15:40Z)
• CodeAgents: A Token-Efficient Framework for Codified Multi-Agent Reasoning in LLMs [16.234259194402163]
  We introduce CodeAgents, a prompting framework that codifies multi-agent reasoning and enables structured, token-efficient planning in multi-agent systems.<n>Results show consistent improvements in planning performance, with absolute gains of 3-36 percentage points over natural language prompting baselines.
  arXiv  Detail & Related papers  (2025-07-04T02:20:19Z)
• AgentGroupChat-V2: Divide-and-Conquer Is What LLM-Based Multi-Agent System Need [35.88121318813734]
  Large language model based multi-agent systems have demonstrated significant potential in social simulation and complex task resolution domains.<n>We introduce AgentGroupChat-V2, a novel framework addressing these challenges through three core innovations.<n>Experiments demonstrate AgentGroupChat-V2's superior performance across diverse domains.
  arXiv  Detail & Related papers  (2025-06-18T13:24:04Z)
• Learning Adaptive Parallel Reasoning with Language Models [70.1745752819628]
  We propose Adaptive Parallel Reasoning (APR), a novel reasoning framework that enables language models to orchestrate both serialized and parallel computations end-to-end.<n> APR generalizes existing reasoning methods by enabling adaptive multi-threaded inference using spawn() and join() operations.<n>A key innovation is our end-to-end reinforcement learning strategy, optimizing both parent and child inference threads to enhance task success rate without requiring predefined reasoning structures.
  arXiv  Detail & Related papers  (2025-04-21T22:29:02Z)
• S*: Test Time Scaling for Code Generation [55.11863577956177]
  We propose S*, the first hybrid test-time scaling framework for code generation.<n>S* substantially improves the coverage and selection accuracy of generated code.
  arXiv  Detail & Related papers  (2025-02-20T09:18:53Z)
• Advancing LLM Reasoning Generalists with Preference Trees [119.57169648859707]
  We introduce Eurus, a suite of large language models (LLMs) optimized for reasoning. Eurus models achieve state-of-the-art results among open-source models on a diverse set of benchmarks.
  arXiv  Detail & Related papers  (2024-04-02T16:25:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.