Evaluating Agents Across Runtime Contracts
Collection
Evaluation traces, instance-paired teacher traces, and the 14 LoRA adapters (persistent and stateless per family) behind the paper. • 16 items • Updated
messages listlengths 3 41 |
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[
{
"role": "system",
"content": "You are a CodeAct-style autonomous agent.\n\nYou solve tasks by alternating between:\n1. Natural-language reasoning (plain text), and\n2. Executable simple Python code blob (inside fenced code blocks).\n\nEach step (output) can include at most 1 (one) code block.\n\nBe concis... |
[
{
"role": "system",
"content": "You are a CodeAct-style autonomous agent.\n\nYou solve tasks by alternating between:\n1. Natural-language reasoning (plain text), and\n2. Executable simple Python code blob (inside fenced code blocks).\n\nEach step (output) can include at most 1 (one) code block.\n\nBe concis... |
[
{
"role": "system",
"content": "You are a CodeAct-style autonomous agent.\n\nYou solve tasks by alternating between:\n1. Natural-language reasoning (plain text), and\n2. Executable simple Python code blob (inside fenced code blocks).\n\nEach step (output) can include at most 1 (one) code block.\n\nBe concis... |
[
{
"role": "system",
"content": "You are a CodeAct-style autonomous agent.\n\nYou solve tasks by alternating between:\n1. Natural-language reasoning (plain text), and\n2. Executable simple Python code blob (inside fenced code blocks).\n\nEach step (output) can include at most 1 (one) code block.\n\nBe concis... |
[
{
"role": "system",
"content": "You are a CodeAct-style autonomous agent.\n\nYou solve tasks by alternating between:\n1. Natural-language reasoning (plain text), and\n2. Executable simple Python code blob (inside fenced code blocks).\n\nEach step (output) can include at most 1 (one) code block.\n\nBe concis... |
[
{
"role": "system",
"content": "You are a CodeAct-style autonomous agent.\n\nYou solve tasks by alternating between:\n1. Natural-language reasoning (plain text), and\n2. Executable simple Python code blob (inside fenced code blocks).\n\nEach step (output) can include at most 1 (one) code block.\n\nBe concis... |
[
{
"role": "system",
"content": "You are a CodeAct-style autonomous agent.\n\nYou solve tasks by alternating between:\n1. Natural-language reasoning (plain text), and\n2. Executable simple Python code blob (inside fenced code blocks).\n\nEach step (output) can include at most 1 (one) code block.\n\nBe concis... |
[
{
"role": "system",
"content": "You are a CodeAct-style autonomous agent.\n\nYou solve tasks by alternating between:\n1. Natural-language reasoning (plain text), and\n2. Executable simple Python code blob (inside fenced code blocks).\n\nEach step (output) can include at most 1 (one) code block.\n\nBe concis... |
[
{
"role": "system",
"content": "You are a CodeAct-style autonomous agent.\n\nYou solve tasks by alternating between:\n1. Natural-language reasoning (plain text), and\n2. Executable simple Python code blob (inside fenced code blocks).\n\nEach step (output) can include at most 1 (one) code block.\n\nBe concis... |
[
{
"role": "system",
"content": "You are a CodeAct-style autonomous agent.\n\nYou solve tasks by alternating between:\n1. Natural-language reasoning (plain text), and\n2. Executable simple Python code blob (inside fenced code blocks).\n\nEach step (output) can include at most 1 (one) code block.\n\nBe concis... |
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Training Traces
Supplementary release for the paper Evaluating Agents Across Runtime Contracts: When Mismatch Costs Efficiency or Quality (IAEval 2026, the NeurIPS 2026 Workshop on Evaluation of Interactive Agents). This dataset
holds the teacher agent traces used to fine-tune the paper's LoRA adapters (see the
sibling cap-sweep-eval-data release and the fourteen adapter repos alongside this one).
6,000 agent traces total (1,000 per family x runtime combination), produced by an LLM teacher solving each of the paper's three agentic task families -- Opaque Knapsack, navigation, and rule diagnosis -- under two interpreter runtime conditions. Each config is a persistent/stateless pair for one family; all six were used to fine-tune the correspondingly-named LoRA adapters.
Key terms
- Persistent runtime: the Python interpreter keeps all variables alive between
agent steps. An agent can write
total_weight += wand it persists to the next turn. - Stateless runtime: the interpreter resets after every step; nothing carries over automatically, so the agent must re-establish any state it needs each turn.
Format
Each line is a {"messages": [...]} chat-format record (system / user / assistant /
tool turns) suitable for direct SFT ingestion. Records include the full CodeAct-style
system prompt, task prompt, the agent's code blocks and tool outputs (including tool
exceptions), through to task completion.
Provenance
Released alongside the paper Evaluating Agents Across Runtime Contracts: When Mismatch Costs Efficiency or Quality (IAEval 2026, the NeurIPS 2026 Workshop on Evaluation of Interactive Agents), to reproduce its reported results.
License
Apache License 2.0.
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