PyTorch checkpoints for the time-series classifiers exposed as tools to the recursive agent in Recursive Agents are Effective Time Series Reasoners (ARTS-RLM). Each subset of the benchmark (capture24 / ltaf / sleep_psg / uk_dale) gets a small specialist classifier the agent can call as a tool while reasoning over long-context signals.

Layout

The repo mirrors the on-disk paths the ARTS-RLM configs already point at, so files can be dropped straight into the project tree.

.
├── results/classifier/
│   ├── chronos2/best_classifier.pt          # Capture24 — Chronos-2 encoder + head (active in configs/rlm)
│   ├── oxwearables/best_classifier.pt       # Capture24 — OxWearables baseline (+ results.json)
│   └── dual/
│       ├── best_classifier.pt               # Capture24 — dual-encoder (pretrain)
│       └── finetune/best_classifier.pt      # Capture24 — dual-encoder (finetune)
├── results/sleep_classifier/
│   ├── sleep_stages/best_classifier.pt      # Sleep-PSG — 5-stage sleep classifier
│   └── arousals/best_classifier.pt          # Sleep-PSG — arousal detector (+ results.json)
├── checkpoints/ltaf/
│   ├── rhythm_resnet1d/best_classifier.pt   # LTAF — rhythm (AF/AFL/NSR/…) ResNet1D
│   └── beats_htf/best_classifier.pt         # LTAF — beat (N/V/S) HTF head
└── results/uk_dale_classifier{,_v2,_v2_ft}/best_classifier.pt
                                             # UK-DALE — appliance window classifier
                                             # v1: active eval/RLM configs
                                             # v2 / v2_ft: arts_rlm_qwen35_9b.toml smoke

Which checkpoint do the configs use?

Subset	Tool	Checkpoint
capture24	`har_classifier`	`results/classifier/chronos2/best_classifier.pt`
ltaf	`ecg_rhythm`	`checkpoints/ltaf/rhythm_resnet1d/best_classifier.pt`
ltaf	`ecg_beats`	`checkpoints/ltaf/beats_htf/best_classifier.pt`
sleep_psg	`sleep_classifier`	`results/sleep_classifier/sleep_stages/best_classifier.pt`
sleep_psg	`sleep_classifier` (arousals)	`results/sleep_classifier/arousals/best_classifier.pt`
uk_dale	`uk_dale_appliance_window`	`results/uk_dale_classifier/best_classifier.pt`

The alternative uk_dale_classifier_v2{,_ft} and results/classifier/{oxwearables,dual} are shipped for the ablation table.

Download

Use the ARTS-RLM helper (drops files into the locations the configs expect):

python scripts/data/download_from_hf.py --dataset classifiers

Or pull individual files directly:

bash

huggingface-cli download nz00shuuuu/arts-rlm-classifiers \
    results/classifier/chronos2/best_classifier.pt \
    --local-dir .

Loading

Each checkpoint is a plain torch.save({...}) dict. They are loaded inside src/models/tools/ via the respective tool's _load_classifier(...) helper — look at src/models/tools/registry.py for the wiring.

python

import torch
state = torch.load("results/classifier/chronos2/best_classifier.pt", map_location="cpu")