CIPHER: A Decoupled Exploration-Selection Framework for Test-Time Scaling of Data Science Agents

• URL: http://arxiv.org/abs/2607.14386v1
• Date: Wed, 15 Jul 2026 22:01:21 GMT
• Title: CIPHER: A Decoupled Exploration-Selection Framework for Test-Time Scaling of Data Science Agents
• Abstract summary: CIPHER is an automated data science agent that leverages test-time scaling through the generation and selection of multiple initial states for concurrent execution.<n>We demonstrate that CIPHER exceeds state-of-the-art performance in matched-model comparisons.<n>Our empirical study characterizes the design space of the Decoupled Exploration-Selection framework.
• Score: 1.4057557845067372
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data science tasks span from closed-ended information extraction to open-ended analysis, presenting significant challenges for automation. Recent AI agents powered by language models show promise for handling such complex tasks. However, existing agents typically rely on a single initial state that conditions the entire agent's execution, making them vulnerable to cascading errors initiated by a suboptimal initial state. To mitigate this, we present CIPHER, an automated data science agent that leverages test-time scaling through the generation and selection of multiple initial states for concurrent execution. Unlike existing works on test-time scaling of AI agents, CIPHER explicitly decouples the generation of candidate initial states from their strategic selection for parallel execution. Through extensive evaluation on two benchmarks (closed-form and open-form tasks), we demonstrate that CIPHER exceeds state-of-the-art performance in matched-model comparisons, and remains competitive against larger-model baselines despite relying on a substantially smaller base LM. Our empirical study characterizes the design space of the Decoupled Exploration-Selection (DES) framework: we quantify how generation strategy, selection strategy, and aggregator model capacity contribute to overall performance, and derive actionable design recommendations for practitioners.

Related papers

• From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents [56.187590188698955]
  Large Language Models have enabled increasingly capable autonomous agents, yet personalization remains critical for making such agents practically useful.<n>Recent benchmarks have begun evaluating personalization in agents, but they largely rely on static preference snapshots, fixed interaction logs, or question answering over predefined user profiles.<n>We introduce IBA-Bench, a benchmark for implicit behavioral alignment constructed from longitudinal interaction histories.<n>We propose IBA-Agent, an agent framework that reconciles conflicting priorities through broad retrieval and trajectory-level alignment.
  arXiv  Detail & Related papers  (2026-08-03T12:52:04Z)
• StarOR: Synergizing Tree Search and Test-Time Reinforcement Learning for Optimization Modeling [13.71352763893512]
  We propose StarOR, a search-and-adaptation framework that couples MCTS with Test-Time Reinforcement Learning for optimization modeling.<n>StarOR decomposes the modeling process into four stages and updates a transient LoRA adapter via GRPO at each non-terminal node.<n> Experiments across five optimization benchmarks show that StarOR achieves state-of-the-art performance even with a 4B backbone.
  arXiv  Detail & Related papers  (2026-06-13T08:46:13Z)
• ThinkBooster: A Unified Framework for Seamless Test-Time Scaling of LLM Reasoning [110.46431027868954]
  ThinkBooster is a framework for seamless test-time compute scaling of large language model (LLM) reasoning.<n>It consists of a modular Python library implementing state-of-the-art TTC scaling strategy and scorer families.<n>A deployable OpenAI-compatible proxy service enables drop-in integration of adaptive reasoning into real-world applications.
  arXiv  Detail & Related papers  (2026-06-05T05:28:46Z)
• Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction [0.02446672595462589]
  We introduce Collider-Bench, a benchmark for evaluating whether language-model agents can reproduce experimental analyses from the Large Hadron Collider (LHC) using only public papers and open scientific software.<n>Agents must therefore rely on physical reasoning, domain knowledge, and trial-and-error to fill these gaps.<n>Our results show that on average no agent reliably beats the physicist-in-the-loop solution.
  arXiv  Detail & Related papers  (2026-05-13T18:00:00Z)
• Agentic Performance at the Edge: Insights from Benchmarking [13.571477369653143]
  We study how much agentic-task quality is lost when model size is constrained by memory, power, and latency budgets.<n>Our core finding is that edge-agent quality is not a simple function of parameter count. Robust deployment depends on the joint design of model choice and tool workflow.
  arXiv  Detail & Related papers  (2026-05-11T11:24:20Z)
• Scaling Agentic Verifier for Competitive Coding [66.11758166379092]
  Large language models (LLMs) have demonstrated strong coding capabilities but still struggle to solve competitive programming problems correctly in a single attempt.<n>Execution-based re-ranking offers a promising test-time scaling strategy, yet existing methods are constrained by either difficult test case generation or inefficient random input sampling.<n>We propose Agentic Verifier, an execution-based agent that actively reasons about program behaviors and searches for highly discriminative test inputs.
  arXiv  Detail & Related papers  (2026-02-04T06:30:40Z)
• PRInTS: Reward Modeling for Long-Horizon Information Seeking [74.14496236655911]
  We introduce PRInTS, a generative PRM trained with dual capabilities.<n>We show that PRInTS enhances information-seeking abilities of open-source models as well as specialized agents.
  arXiv  Detail & Related papers  (2025-11-24T17:09:43Z)
• EmbodiedBrain: Expanding Performance Boundaries of Task Planning for Embodied Intelligence [17.644658293987955]
  Embodied AI agents are capable of robust spatial perception, effective task planning, and adaptive execution in physical environments.<n>Current large language models (LLMs) and multimodal LLMs (MLLMs) for embodied tasks suffer from key limitations.<n>We propose EmbodiedBrain, a novel vision-language foundation model available in both 7B and 32B parameter sizes.
  arXiv  Detail & Related papers  (2025-10-23T14:05:55Z)
• Thinking Longer, Not Larger: Enhancing Software Engineering Agents via Scaling Test-Time Compute [61.00662702026523]
  We propose a unified Test-Time Compute scaling framework that leverages increased inference-time instead of larger models.<n>Our framework incorporates two complementary strategies: internal TTC and external TTC.<n>We demonstrate our textbf32B model achieves a 46% issue resolution rate, surpassing significantly larger models such as DeepSeek R1 671B and OpenAI o1.
  arXiv  Detail & Related papers  (2025-03-31T07:31:32Z)
• Generative Judge for Evaluating Alignment [84.09815387884753]
  We propose a generative judge with 13B parameters, Auto-J, designed to address these challenges. Our model is trained on user queries and LLM-generated responses under massive real-world scenarios. Experimentally, Auto-J outperforms a series of strong competitors, including both open-source and closed-source models.
  arXiv  Detail & Related papers  (2023-10-09T07:27:15Z)

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.