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autoresearch-arena/bbo_noisy_continuous | # Noisy Continuous Black-Box Optimizer Design
Your task is to design a reproducible, noise-aware optimizer that minimizes 10-dimensional continuous black-box objectives with smooth periodic, multimodal structure. You inherit a weak uniform-random baseline, and every run uses the box `[-5, 5]^10` with a strict budget o... | Design a noise-aware optimizer for multimodal continuous functions under a fixed query budget and a comparable visible/sealed normalized anytime-final metric. | [] | 1.3 | {
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"description": "Design a noise-aware optimizer for multimodal continuous functions under a fixed query budget and a comparable visible/sealed normalized anytime-final metric.",
... | {} | [
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"tests/grade.py",
"tests/heldout/bbo_harness.py",
"tests/hel... | bbo_noisy_continuous |
autoresearch-arena/bbo_simopt_inventory | # Query-efficient stochastic inventory policy search
Design a reproducible optimizer for four normalized controls of a replenishment policy under stochastic demand and lead times. Minimize simulated operating cost within 48 queries over `[0, 1]^4`. You inherit a uniform-random starter and receive noisy objective feedb... | Design a query-efficient optimizer for a stochastic replenishment policy across varied cost and demand regimes. | [] | 1.3 | {
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"name": "autoresearch-arena/bbo_simopt_inventory",
"description": "Design a query-efficient optimizer for a stochastic replenishment policy across varied cost and demand regimes.",
"authors": [
{
"name": "haoting... | {} | [
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"environment/selfcheck.py",
"instruction.md",
"solution/reference_optimizer.py",
"solution/solve.... | bbo_simopt_inventory |
autoresearch-arena/bigann_filtered_vector_search | # Filtered nearest-neighbour search at 10M scale
A photo library holds **10,000,000 images**. Each is a 192-dimensional `uint8` embedding plus a set
of metadata tags (uploader, camera, place, year, and so on). A query arrives as an embedding plus a
**tag predicate**: one or two tags. An image is a valid answer only if... | Build a filtered-ANN index/search solver over the real Big-ANN yfcc-10M library (10M x 192 uint8 vectors, 200,386 tags, 108M tag entries); graded by wall-clock throughput of one batch call at recall@10>=0.90 on a sealed hidden workload. | [] | 1.3 | {
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"name": "autoresearch-arena/bigann_filtered_vector_search",
"description": "Build a filtered-ANN index/search solver over the real Big-ANN yfcc-10M library (10M x 192 uint8 vectors, 200,386 tags, 108M tag entries); graded by wall-... | {} | [
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"solu... | bigann_filtered_vector_search |
autoresearch-arena/citywide_signal_coordination | # City-Wide Signal Coordination
You inherit a full-city traffic microsimulation — a real metropolitan road network with
~600 signalised junctions and a synthetic morning-peak demand — running on its original,
un-optimised static signal timing. Improve the signal control so the whole
city moves better under load, minim... | Coordinate signal timing across a full metropolitan road network (~600 signalised junctions) to minimise a weighted delay/teleport/stop cost under heavy demand; you submit a signal program / online controller, re-run on sealed hidden load and seed settings, lower is better. | [] | 1.3 | {
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"description": "Coordinate signal timing across a full metropolitan road network (~600 signalised junctions) to minimise a weighted delay/teleport/stop cost under heavy de... | {} | [
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"instruction.md",
"task.toml",
"tests/Dockerfile",
"tests/anchors... | citywide_signal_coordination |
autoresearch-arena/clevr_cogent_grpo_qwen2vl | # Train Qwen2-VL-2B with GRPO so it generalizes off the training distribution
You inherit a deliberately weak reinforcement-learning (GRPO) training project for the
vision-language model `Qwen2-VL-2B-Instruct`: the starting point in `/app/methods/main/` trains on
the provided counting-VQA split with a trivial reward a... | Train Qwen2-VL-2B-Instruct with GRPO on CLEVR-CoGenT-A so it generalizes to hidden CLEVR-CoGenT-B and SuperCLEVR; graded by mean keyword-matching counting accuracy. | [] | 1.3 | {
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"name": "autoresearch-arena/clevr_cogent_grpo_qwen2vl",
"description": "Train Qwen2-VL-2B-Instruct with GRPO on CLEVR-CoGenT-A so it generalizes to hidden CLEVR-CoGenT-B and SuperCLEVR; graded by mean keywor... | {} | [
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"task.toml",
"tests/Dockerfile",
"tests/anchors.json"... | clevr_cogent_grpo_qwen2vl |
autoresearch-arena/cxr_ood_triage_policy | # Chest X-ray triage under cross-site distribution shift
You inherit a radiology triage component from a teammate. A fixed, frozen chest-radiograph classifier already emits per-finding probabilities for every study; the teammate's stop-gap refers a study to a radiologist whenever the largest critical-finding probabili... | Improve a weak chest-X-ray triage policy over frozen classifier scores. You submit triage(cases, resources, seed); a sealed verifier refers the top-risk studies under a fixed shared budget across an in-domain and an unseen site, and scores mean sensitivity minus a Brier (calibration) penalty minus a worst-site gap. Raw... | [] | 1.3 | {
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"name": "autoresearch-arena/cxr_ood_triage_policy",
"description": "Improve a weak chest-X-ray triage policy over frozen classifier scores. You submit triage(cases, resources, seed); a sealed verifier refers the top-risk studies u... | {} | [
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"instruction.md",
"solution/methods/README.md",
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autoresearch-arena/device_iv_regime_extrapolation_v1 | # Commit terminal-current numbers for a diode lot beyond the safe test window
You are an AI agent assisting the device engineer who must qualify a new
power-diode lot at a semiconductor supplier. The probe station covers only a safe
bias/temperature window — 290–325 K, reverse bias to −3.6 V — but the
qualification si... | Power-device qualification sign-off: submit a prediction method that turns safe-window I-V/temperature measurements of a diode lot into terminal-current commitments at mission-profile extremes (high temperature, deep reverse bias); a sealed evaluation re-runs it on an unseen lot drawn from the same population as the pr... | [] | 1.3 | {
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"name": "autoresearch-arena/device_iv_regime_extrapolation_v1",
"description": "Power-device qualification sign-off: submit a prediction method that turns safe-window I-V/temperature measurements of a diode lot into terminal-curre... | {} | [
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"environment/methods/main/solver.py",
"environment/run_solver.py",
... | device_iv_regime_extrapolation |
autoresearch-arena/discoveryworld_agent_harness_low2 | # Improve a Scientific Discovery Agent
You inherit a weak but working ReAct harness that drives a fixed language model
through open-ended scientific discovery scenarios in a simulated world. Improve
its reasoning, experimentation, memory, action selection, and the explanatory
conclusions it states; the submitted harne... | Improve a ReAct harness over a fixed language model on low-baseline Space Sick and Chemistry Challenge tasks. | [] | 1.3 | {
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"name": "autoresearch-arena/discoveryworld_agent_harness_low2",
"description": "Improve a ReAct harness over a fixed language model on low-baseline Space Sick and Chemistry Challenge tasks.",
"authors": [
{
"name... | {} | [
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autoresearch-arena/eda_gate_sizing | # Optimize logic gate sizing under timing and design-rule penalties
## Background
A chip is built from millions of tiny logic gates, and the same gate comes in several sizes that
do the identical job. A bigger one switches faster, but burns more power and is heavier for the
gate feeding it to push. Picking a size for... | Choose a library cell per instance to minimize power under timing/DRV constraints on placed netlists (CircuitOps IR tables); submit an algorithm, graded on sealed hidden designs, lower is better. | [] | 1.3 | {
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"description": "Choose a library cell per instance to minimize power under timing/DRV constraints on placed netlists (CircuitOps IR tables); submit an algorithm, graded on sealed hidde... | {} | [
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"environment/methods/main/solver.py",
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"environment/self... | eda_gate_sizing |
autoresearch-arena/feeder_phase_impedance_inversion_v1 | # Repair a feeder's phase and impedance records from smart-meter archives
You are an AI agent assisting a distribution planning engineer at a mid-size
utility with a solar interconnection queue. Before any hosting-capacity or voltage-drop
study can be signed off, the feeder model has to be calibrated: the GIS
says whi... | Distribution-feeder model calibration before hosting-capacity studies: submit a method that jointly repairs per-customer phase labels, per-segment conductor impedances and the regulator tap from magnitude-only AMI archives plus head SCADA; a sealed evaluation re-runs it on a larger fleet of unseen feeders drawn from th... | [] | 1.3 | {
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"name": "autoresearch-arena/feeder_phase_impedance_inversion_v1",
"description": "Distribution-feeder model calibration before hosting-capacity studies: submit a method that jointly repairs per-customer phase labels, per-segment c... | {} | [
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autoresearch-arena/finscope_dcf_valuation | # FinScope coverage desk — forward value-driver model
You have inherited a buy-side equity-research desk's **intrinsic-value model**. The desk
values companies with a fixed, trusted discounted-cash-flow (DCF) engine that turns a set of
**forward value-drivers** into a per-share value; the open problem is **forecasting... | Forecast forward value-drivers for a coverage universe from a messy fundamentals panel; graded by median absolute DCF valuation error on a sealed held-out tier. | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
"/app/methods"
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"task": {
"name": "autoresearch-arena/finscope_dcf_valuation",
"description": "Forecast forward value-drivers for a coverage universe from a messy fundamentals panel; graded by median absolute DCF valuation error on a sealed held-out tier."... | {} | [
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"environment/fit_calibration.py",
"environme... | finscope_dcf_valuation |
autoresearch-arena/flashattention_varlen_feature_full_vjp_speedup | # Noncausal Global Extreme-GQA Ragged Attention Full VJP
Optimize an inherited eager implementation of packed ragged GQA
cross-attention with extreme query-head sharing, bidirectionally unequal Q/K
lengths, per-sequence ALiBi, softcapping, non-dense packed views, and complete
Q/K/V vector-Jacobian products. Maximize r... | Optimize six preregistered D160 noncausal-global ragged attention strata per panel with 48–96:1 GQA, ALiBi, softcap, non-dense packed views, and complete Q/K/V VJP. | [] | 1.3 | {
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"description": "Optimize six preregistered D160 noncausal-global ragged attention strata per panel with 48–96:1 GQA, ALiBi, softcap, non-dense packed vie... | {} | [
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"environment/problems/visible_baseline_manifest.json",
"environment/problems/visible_cas... | flashattention_varlen_feature_full_vjp_speedup |
autoresearch-arena/flashfftconv_multistream_gated_forward_speedup | # Multi-stream Gated FlashFFTConv Forward
Optimize multiple independent gated causal convolutions across the published long-convolution range. The final implementation is re-run on sealed stream counts, shapes, gate distributions, kernel scales, lengths, and seeds.
## Hard Constraints
- Edit only `/app/methods/main/... | Optimize independent multi-stream gated causal convolutions in the published FP16 D=768, length-2048 regime across multiple batch/stream/head factorizations on NVIDIA H100. | [] | 1.3 | {
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"description": "Optimize independent multi-stream gated causal convolutions in the published FP16 D=768, length-2048 regime across multiple batch/stream/... | {} | [
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"enviro... | flashfftconv_multistream_gated_forward_speedup |
autoresearch-arena/game2048_policy_search | # 2048 Strategy
You inherit a weak Python policy that plays deterministic seeded 2048 games. Improve
the policy using repeated experiments on the public seed suite; your submitted policy
is replayed from scratch on sealed seeds from the same generator for scoring.
## Hard Constraints
- Edit `methods/main/policy.py`;... | Improve a Python policy for seeded 2048 games, using public games for feedback and sealed same-distribution seeds for final scoring. | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
"/app/methods"
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"task": {
"name": "autoresearch-arena/game2048_policy_search",
"description": "Improve a Python policy for seeded 2048 games, using public games for feedback and sealed same-distribution seeds for final scoring.",
"authors": [
{
... | {} | [
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"environment/visible_seeds.json",
"instruction.md",
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"tests/.dockerignore"... | game2048_policy_search |
autoresearch-arena/johnson1991_leighton_graph_coloring | # Drive coloring conflicts to zero at a fixed color budget
You inherit a TabuCol graph-coloring baseline. For every graph you are given a **fixed
color budget** `target_k`. Assign each vertex a color in `{0, 1, ..., target_k-1}` so as
to **minimize the number of monochromatic edges** (edges whose two endpoints share a... | Minimize coloring conflicts at a fixed color budget on real, hard DIMACS/COLOR02 graphs (Leighton le450, Johnson DSJC, DSJR; n=450..1000). target_k is each graph's PUBLISHED best-known chromatic number, so a proper 0-conflict coloring provably exists but sits at the coloring frontier. The verifier reruns solve(data) on... | [] | 1.3 | {
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"name": "autoresearch-arena/johnson1991_leighton_graph_coloring",
"description": "Minimize coloring conflicts at a fixed color budget on real, hard DIMACS/COLOR02 graphs (Leighton le450, Johnson DSJC, DSJR; n=450..1000). target_k ... | {} | [
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"... | johnson1991_leighton_graph_coloring |
autoresearch-arena/lean_formal_proof_workflow_design | # Design a verifier-guided Lean 4 proof workflow
You inherit a small compiler-guided proof workflow over a pinned language model. Improve the workflow in `/app/methods/main/` so it completes canonical Lean 4 theorem statements reliably under a tight inference and compiler-feedback budget. The verifier copies your fina... | Design one verifier-guided workflow that proves fixed formal theorem statements with a pinned language model under per-problem inference, compiler-check, and wall-clock budgets. | [] | 1.3 | {
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"description": "Design one verifier-guided workflow that proves fixed formal theorem statements with a pinned language model under per-problem inference, compiler-che... | {} | [
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autoresearch-arena/legal_matter_caseload_regulatory | # Answer legal questions from the underlying case files
You are the analyst covering six unrelated legal matters. Each has its own folder
of case documents under `tools/world_corpus/`:
| folder | matter |
|---|---|
| `w421_telemarketing` | Senior Living Lending — TCPA / TSR telemarketing compliance, mini-TCPA state l... | Work six unrelated legal matters end-to-end on real case files (1,000+ documents: Senior Living Lending telemarketing/TCPA compliance, Harborview spinoff + cross-border data transfer, a BlueAnchor/BlueLNG JV with anti-SLAPP and shipyard-delay disputes, Northstar GDPR, Livyra FDA drug labelling, and TAC tariffs/force ma... | [] | 1.3 | {
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"name": "autoresearch-arena/legal_matter_caseload_regulatory",
"description": "Work six unrelated legal matters end-to-end on real case files (1,000+ documents: Senior Living Lending telemarketing/TCPA compliance, Harborview spino... | {} | [
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"environment/tools/world_corpus/w416_tariff/8-K, 10-K, 10-Q Statements, etc./10-K For the fiscal year ended December 31,... | legal_matter_caseload_regulatory |
autoresearch-arena/locomo_longterm_memory | # Redesign a conversational memory system that generalizes to unseen conversations
You inherit a minimal long-term-memory system for LLM agents: it
extracts atomic memory entries from multi-session conversations with an LLM,
indexes them with BM25 only (k=5, 8-entry context), and answers questions with
one concise LLM... | Redesign a minimal conversational memory system (LLM extraction + BM25-only retrieval + concise answering) so it generalizes: iterate freely on 4 visible multi-session conversations (812 QA); a sealed verifier re-runs your system on 6 unseen conversations (1,174 QA) and scores token-level F1 (higher is better). | [] | 1.3 | {
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"name": "autoresearch-arena/locomo_longterm_memory",
"description": "Redesign a minimal conversational memory system (LLM extraction + BM25-only retrieval + concise answering) so it generalizes: iterate freely on 4 visible multi-s... | {} | [
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"environment/methods/main/memsys/answering.... | locomo_longterm_memory |
autoresearch-arena/lung_fewshot_celltype_annotation | # Few-Shot Cross-Donor Cell-Type Annotation
You inherit a weak starter classifier trained from five labeled cells per class and a much larger unlabeled single-cell expression pool. Improve the reusable annotation method to maximize macro-F1; the submitted source is rerun from scratch on donor-disjoint cells in a seale... | Improve a five-shot lung cell-type classifier using unlabeled scRNA-seq cells; graded by macro-F1 after replay on donor-disjoint sealed cells. | [] | 1.3 | {
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"name": "autoresearch-arena/lung_fewshot_celltype_annotation",
"description": "Improve a five-shot lung cell-type classifier using unlabeled scRNA-seq cells; graded by macro-F1 after replay on donor-d... | {} | [
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"environment/bwrap_guard.c",
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autoresearch-arena/mbff_banking_placement | # Optimize multibit flip-flop banking and placement
You inherit a deliberately weak **template** for an industrial-style EDA placement problem:
multibit flip-flop (MBFF) banking and placement. Given a placement testcase, your method must
emit a flip-flop placement + pin-mapping that minimizes a weighted cost (power, a... | Optimize multibit flip-flop banking and placement to minimize a weighted power/area/timing/displacement cost on industrial-style testcases; graded on sealed hidden cases, lower is better. | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
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"name": "autoresearch-arena/mbff_banking_placement",
"description": "Optimize multibit flip-flop banking and placement to minimize a weighted power/area/timing/displacement cost on industrial-style testcases; graded on sealed hidd... | {} | [
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autoresearch-arena/multiview_dense_reconstruction | # Multi-view point-cloud reconstruction post-processing
Improve a Python post-processor that combines noisy three-dimensional observations from several views into one clean scene cloud. The objective is low geometric error on unseen cases while retaining broad surface coverage and fine detail.
## Hard Constraints
- ... | Register several noisy, partially-overlapping 3D point sets that are not in a common coordinate system -- each view carries an unknown rigid pose relative to the first -- and fuse them, with per-point confidences, into one clean scene cloud, evaluated by a separate sealed verifier. Synthetic point sets only: no images,... | [] | 1.3 | {
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"environment/public/observations/visible_case_003.json",
"environment/public/... | multiview_dense_reconstruction |
autoresearch-arena/paged_ragged_gqa_decode_speedup | # Paged Ragged GQA Decode Attention
You inherit a correct batch-vectorized PyTorch implementation of grouped-query decode attention over a paged, variable-length KV cache. Make it as fast as possible while it keeps computing the same thing within the accuracy the verifier enforces.
## Hard Constraints
- Keep the cal... | Optimize FP16/BF16 grouped-query decode attention over a paged, ragged KV cache on NVIDIA H100. | [] | 1.4 | {
"schema_version": "1.4",
"artifacts": [
"/app/methods"
],
"task": {
"name": "autoresearch-arena/paged_ragged_gqa_decode_speedup",
"description": "Optimize FP16/BF16 grouped-query decode attention over a paged, ragged KV cache on NVIDIA H100.",
"authors": [
{
"name": "riken"
... | {} | [
"environment/.dockerignore",
"environment/Dockerfile",
"environment/child_eval.py",
"environment/docker-compose.yaml",
"environment/methods/experiment_log.md",
"environment/methods/main/solver.py",
"environment/problems/visible_spec.json",
"environment/protocol.py",
"environment/restricted_runner.py... | paged_ragged_gqa_decode_speedup |
autoresearch-arena/pbmc_batch_correction | # Batch-integrated cell embedding from scATAC-seq peaks (PBMC)
Single-cell data is collected in separate batches (samples, labs, runs), and technical batch
effects can dominate and obscure real biology. You inherit a no-correction **PCA** embedding (which
keeps batch variation) and must produce a low-dimensional embed... | Produce a batch-integrated cell embedding from binary PBMC scATAC-seq peaks; graded by cell-type NMI (scIB Leiden protocol) on a sealed split. | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
"/app/methods"
],
"task": {
"name": "autoresearch-arena/pbmc_batch_correction",
"description": "Produce a batch-integrated cell embedding from binary PBMC scATAC-seq peaks; graded by cell-type NMI (scIB Leiden protocol) on a sealed split.",
"authors": [
... | {} | [
"environment/Dockerfile",
"environment/data/README.md",
"environment/data/download_data.py",
"environment/methods/main/solver.py",
"environment/selfcheck.py",
"instruction.md",
"task.toml",
"tests/Dockerfile",
"tests/anchors.json",
"tests/download_data.py",
"tests/grade.py",
"tests/heldout/MAN... | pbmc_batch_correction |
autoresearch-arena/perturbed_cell_morphology_generation | # Six-Channel Cell Morphology Generation
Generate perturbed-cell morphology from untreated controls and biological condition embeddings. You inherit a deterministic control-passthrough solver and should minimize overall FID; the verifier re-runs your saved method on sealed test cells.
## Hard Constraints
- Work only... | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
"/app/methods",
"/app/submission"
],
"task": {
"name": "autoresearch-arena/perturbed_cell_morphology_generation",
"authors": [
{
"name": "aiminglab"
}
]
},
"agent": {
"timeout_sec": 43200,
"network_mode": "no-network"
... | {} | [
".gitignore",
"environment/.dockerignore",
"environment/Dockerfile",
"environment/copy_check.py",
"environment/docker-compose.yaml",
"environment/fid_metric.py",
"environment/methods/main/solver.py",
"environment/prepare_data.py",
"environment/requirements.lock.txt",
"environment/selfcheck.py",
... | perturbed_cell_morphology_generation | |
autoresearch-arena/protein_ligand_cofolding | # Protein-Ligand Co-folding
You inherit a deliberately weak inference pipeline that predicts a protein-ligand complex from protein sequence, ligand SMILES, and a precomputed MSA. Improve its inference-time success rate under the pinned physical-validity checks and a 2 Å ligand-RMSD threshold; your submitted method is ... | Improve an inference-only protein-ligand co-folding pipeline; the isolated verifier re-runs submitted source on a sealed structural split and scores physically valid, geometrically accurate poses. | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
"/app/methods/main"
],
"task": {
"name": "autoresearch-arena/protein_ligand_cofolding",
"description": "Improve an inference-only protein-ligand co-folding pipeline; the isolated verifier re-runs submitted source on a sealed structural split and scores physi... | {} | [
"environment/.dockerignore",
"environment/Dockerfile",
"environment/docker-compose.yaml",
"environment/methods/main/cofold_utils.py",
"environment/methods/main/solver.py",
"environment/metric.py",
"environment/requirements.lock",
"environment/selfcheck.py",
"environment/source_contract.py",
"instr... | protein_ligand_cofolding_posebusters |
autoresearch-arena/qec_decoder_arena | # Beat MWPM: decode quantum error-correction color codes
You inherit a plain PyMatching (minimum-weight perfect matching) decoder and a
labeled development pack for four superdense **color-code** memory experiments.
Your decoder is then re-run by a sealed grader on four HELD-OUT color settings
(same code family, calib... | Decode quantum error-correction color codes. You inherit a plain-PyMatching decoder and submit decode(setting, dets) -> predicted logical-observable flips; a sealed grader re-runs it on four held-out superdense color-code settings (d5 X, d5 Z, d7, d9), compares predictions to hidden ground-truth flips (LER = fraction o... | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
"/app/methods"
],
"task": {
"name": "autoresearch-arena/qec_decoder_arena",
"description": "Decode quantum error-correction color codes. You inherit a plain-PyMatching decoder and submit decode(setting, dets) -> predicted logical-observable flips; a sealed g... | {} | [
"environment/Dockerfile",
"environment/arena_harness.py",
"environment/methods/main/solver.py",
"environment/selfcheck.py",
"environment/settings_visible/color_d5/dev.npz",
"environment/settings_visible/color_d5/eval.npz",
"environment/settings_visible/color_d5/meta.json",
"environment/settings_visibl... | qec_decoder_arena |
autoresearch-arena/qlib_alpha_factor_icir | # Build a cross-sectional return-prediction model
You are given a real-market-derived **daily cross-sectional factor panel** with anonymized
identifiers (opaque `datetime`/`instrument` IDs, 360 factor columns and a forward-return
`label`). Train a method that outputs a continuous `score` per instrument-day; it is scor... | Build a cross-sectional return-prediction model on an anonymized daily factor panel; graded by ICIR on a hidden later period. | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
"/app/methods"
],
"task": {
"name": "autoresearch-arena/qlib_alpha_factor_icir",
"description": "Build a cross-sectional return-prediction model on an anonymized daily factor panel; graded by ICIR on a hidden later period.",
"authors": [
{
... | {} | [
"environment/Dockerfile",
"environment/methods/main/solver.py",
"environment/prepare_data.py",
"environment/selfcheck.py",
"instruction.md",
"task.toml",
"tests/Dockerfile",
"tests/anchors.json",
"tests/grade.py",
"tests/prepare_data.py",
"tests/test.sh"
] | qlib_alpha_factor_icir |
campaign-003/glass_cutting_v1 | # Cut the glass order out of as few plates as possible
You run the cutting floor of a flat-glass plant. Orders arrive as a **batch** of rectangular glass
items, and you cut them out of large rectangular stock **plates** (6000 x 3210 mm) on a guillotine
cutting line. Every cut runs edge to edge, so each plate is carved... | Cut a batch of rectangular glass items out of the fewest 6000x3210 stock plates on a guillotine line, minimising wasted area, on an unseen unseen industrial instance. Solutions must respect exact 3-stage guillotine cuts, defect avoidance, min/max cut distances, min-waste, and per-stack production sequence; the official... | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
"/app/methods"
],
"task": {
"name": "campaign-003/glass_cutting_v1",
"description": "Cut a batch of rectangular glass items out of the fewest 6000x3210 stock plates on a guillotine line, minimising wasted area, on an unseen unseen industrial instance. Soluti... | {} | [
".gitignore",
"environment/Dockerfile",
"environment/assets_manifest.json",
"environment/build_assets.sh",
"environment/checker_src/include/GlassDefect.h",
"environment/checker_src/include/GlassItem.h",
"environment/checker_src/include/GlassNode.h",
"environment/checker_src/include/GlassPlate.h",
"e... | roadef_glass_cutting |
autoresearch-arena/switch_budget_candidate_routing | # Switch-budgeted candidate routing
You inherit a weak persistent router over ten aligned candidate tracks for each queried point. Improve the general routing and visibility method to maximize mean per-video Average Jaccard while using at most four semantic-state changes after each point's query frame. The verifier re... | Optimize semantic candidate routing and visibility under a four-switch temporal budget, scored by mean per-video Average Jaccard on a sealed split. | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
"/app/methods",
"/app/experiment_log.md"
],
"task": {
"name": "autoresearch-arena/switch_budget_candidate_routing",
"description": "Optimize semantic candidate routing and visibility under a four-switch temporal budget, scored by mean per-video Average J... | {} | [
"environment/.dockerignore",
"environment/Dockerfile",
"environment/bwrap_guard.c",
"environment/bwrap_guard_selfcheck.sh",
"environment/data/visible/index.json",
"environment/data/visible/inputs/case_027f340b25d016930d63.npz",
"environment/data/visible/inputs/case_02b38a8b7262158cd7a5.npz",
"environm... | robotap_switch_budget_candidate_routing_optimization |
autoresearch-arena/splash_attention_strata_speedup | # Make masked grouped-query attention fast on a TPU v6e chip
## Background
Attention is the dominant cost in serving and training large language models. Production
configurations rarely run the textbook dense form: queries share key/value heads in groups
(GQA/MQA), long contexts use sliding windows, some models cap a... | Given naive-XLA masked GQA attention on TPU v6e, write a faster kernel; graded by per-case speedup on sealed shape strata, correctness gated. | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
"/app/methods"
],
"task": {
"name": "autoresearch-arena/splash_attention_strata_speedup",
"description": "Given naive-XLA masked GQA attention on TPU v6e, write a faster kernel; graded by per-case speedup on sealed shape strata, correctness gated.",
"aut... | {} | [
"environment/Dockerfile",
"environment/data/visible_strata.json",
"environment/docker-compose.yaml",
"environment/eval/attn_eval.py",
"environment/eval/baseline_impl.py",
"environment/methods/main/attention.py",
"environment/selfcheck.py",
"instruction.md",
"task.toml",
"tests/Dockerfile",
"test... | splash_attention_strata_speedup |
autoresearch-arena/tddn_contraction_planning | # Plan the contraction order for exact decision-diagram circuit equivalence checking
Two quantum circuits are checked for exact equivalence by contracting a network of Tensor
Decision Diagrams (TDDs) down to a single tensor and testing it against the identity. Every
contraction order reaches the same verdict, but the ... | Deterministic inference-time planning for exact Tensor Decision Diagram Network contractions in quantum-circuit equivalence checking. | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
"/app/methods"
],
"task": {
"name": "autoresearch-arena/tddn_contraction_planning",
"description": "Deterministic inference-time planning for exact Tensor Decision Diagram Network contractions in quantum-circuit equivalence checking.",
"authors": [
... | {} | [
"environment/Dockerfile",
"environment/data/README.md",
"environment/data/visible_cases.json",
"environment/evaluate_visible.py",
"environment/methods/main/solver.py",
"environment/selfcheck.py",
"environment/tdd_backend.py",
"environment/tdd_runtime/CMakeLists.txt",
"environment/tdd_runtime/Definit... | tddn_contraction_planning |
autoresearch-arena/teacher_student_math_posttraining | # Mathematical Post-Training
Improve the mathematical-reasoning accuracy of an inherited 1.7B LLM, with potentially useful a sanitized training corpus, using a fixed local 8B teacher LLM and a weak answer-only starter. Optimize exact integer accuracy; your submitted merged model will re-run on sealed hidden problems f... | Post-train a fixed Qwen3-1.7B Base student with a fixed 8B teacher and a sanitized mathematical-reasoning corpus; submit merged model weights scored by sealed exact-answer accuracy. | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
"/app/submission",
"/app/methods",
"/app/experiment_log.md"
],
"task": {
"name": "autoresearch-arena/teacher_student_math_posttraining",
"description": "Post-train a fixed Qwen3-1.7B Base student with a fixed 8B teacher and a sanitized mathematical-r... | {} | [
"environment/.dockerignore",
"environment/Dockerfile",
"environment/docker-compose.yaml",
"environment/evaluator/Dockerfile",
"environment/evaluator/visible_server.py",
"environment/methods/main/README.md",
"environment/methods/main/build_answer_sft.py",
"environment/methods/main/common.py",
"enviro... | teacher_student_math_posttraining |
autoresearch-arena/tidal_friction_inverse | # Sparse-Gauge Tidal Friction Field Inversion
Design a calibration method for the spatially varying seabed friction field of a tidal basin, starting from a deliberately weak homogeneous-friction solver. Minimize complex tidal-elevation RMSE at unobserved gauges; your submitted method is re-run on sealed basins and sea... | Estimate a heterogeneous seabed friction field from sparse tide gauges and generalize to sealed gauges. | [] | 1.0 | {
"schema_version": "1.0",
"task": {
"name": "autoresearch-arena/tidal_friction_inverse",
"description": "Estimate a heterogeneous seabed friction field from sparse tide gauges and generalize to sealed gauges.",
"authors": [
{
"name": "wang"
}
]
},
"environment": {
"cpus"... | {} | [
"environment/DATA_CARD.md",
"environment/Dockerfile",
"environment/methods/main/solver.py",
"environment/prepare_data.py",
"environment/problem.py",
"environment/selfcheck.py",
"instruction.md",
"task.toml",
"tests/Dockerfile",
"tests/anchors.json",
"tests/grade.py",
"tests/prepare_heldout.py"... | tidal_friction_inverse |
autoresearch-arena/trifinger_offline_rl_push | # Learn a TriFinger cube-pushing policy from a fixed offline dataset
You inherit an **offline reinforcement learning** problem on the TriFinger robot simulator: a
three-fingered robot must push a cube to a target position, and you may only learn from a **fixed,
pre-collected dataset** of past trajectories (no live sim... | Learn a cube-pushing policy for the TriFinger robot from a fixed offline dataset (no live simulator access during training). You submit train(dataset_dir, out_dir) that saves a checkpoint plus a Policy class; a sealed verifier reloads the checkpoint and re-runs it on a hidden batch of episodes, replaying the recorded a... | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
"/app/methods",
"/app/submission"
],
"task": {
"name": "autoresearch-arena/trifinger_offline_rl_push",
"description": "Learn a cube-pushing policy for the TriFinger robot from a fixed offline dataset (no live simulator access during training). You submit... | {} | [
"environment/.dockerignore",
"environment/Dockerfile",
"environment/methods/main/solver.py",
"environment/prepare_visible.py",
"environment/selfcheck.py",
"environment/trifinger_score.py",
"instruction.md",
"task.toml",
"tests/.dockerignore",
"tests/Dockerfile",
"tests/anchor_checkpoints/README.... | trifinger_offline_rl_push_refined |
autoresearch-arena/warehouse_robot_macro_routing | # Program the cleanup robot to sort every ball into its basket
The office floor is a mess. Balls of every colour are scattered across an
`N x N` warehouse grid criss-crossed by shelving walls, and each colour has one
matching basket somewhere on the floor. You have exactly one robot and one
controller. The robot under... | Drive a warehouse cleanup robot (forward / turn / swap, plus a record-once / replay-many macro button) to sort every ball into its matching basket in as few button presses as possible, on an unseen maze-and-ball layout drawn from the AHC066 generator. Macro expansion is charged against the move budget T but a replay st... | [] | 1.3 | {
"schema_version": "1.3",
"artifacts": [
"/app/methods"
],
"task": {
"name": "autoresearch-arena/warehouse_robot_macro_routing",
"description": "Drive a warehouse cleanup robot (forward / turn / swap, plus a record-once / replay-many macro button) to sort every ball into its matching basket in as f... | {} | [
".gitignore",
"environment/Dockerfile",
"environment/methods/main/run.sh",
"environment/methods/main/solution.py",
"environment/reference/visible_rank1_lengths.json",
"environment/selfcheck.py",
"environment/tools/Cargo.lock",
"environment/tools/Cargo.toml",
"environment/tools/build_tools.sh",
"en... | warehouse_robot_macro_routing |
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