DeCLaRe Lab · NTU Singapore

Lab notes

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2026

A ScrambleToolBench episode: an agent probes scrambled commands, follows responses to a passcode, submits it and reuses tool knowledge in the next task Lab note · September 2026

ScrambleToolBench: the next step is already in the map

An agent discovers what its tools do. Their names change, and it starts searching again—even when its earlier observations point to the next call.

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A successful MNIST-PRO episode: four glimpses along a handwritten zero, ending with the correct answer at step 9 Lab note · September 2026

MNIST-PRO: seeing the strokes is not enough

When an agent gets a digit wrong, did it miss the evidence or struggle to put the glimpses together? We use MNIST-PRO to tell these failures apart.

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IDEAgent quality-diversity search framework Lab note · August 2026

IDEAgent: Agentic Quality-Diversity Search

IDEAgent searches for a diverse set of candidate research ideas and records how each candidate changes during refinement.

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PODS oscillatory data-volume scheduling overview Lab note · May 2026

Toward Efficient Data-Centric Training, Part II

PODS changes how much data is used at each stage of training instead of fixing one selection ratio.

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Data Agent training-loop overview Lab note · May 2026

Toward Efficient Data-Centric Training, Part I

Data Agent updates its data-selection policy as the target model trains.

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delta-mem architecture overview Lab note · May 2026

δ-mem: Efficient Online Memory for LLMs

δ-mem keeps a compact state during inference and uses it to modify attention in a frozen language model.

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