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Augmentor Policy Handoffs

Synthetic SFT

sft.json

Multi-turn tool-using investigation as a clean demonstration.

id
ml_research_trajectory_augmentor_policy_handoffs_001
domain
reinforcement_learning_research
task_type
multi_turn_autonomous_research_investigation
variant
A_reasoning_externalized

provenance

source:human-steered research session, 2026-07-31 to 2026-08-02
construction:The human researcher's steering turns and the orchestrator's execution turns were collapsed into a single assistant agent. All experiment results, metrics, theorem checks, and falsifications are taken from the real runs. Two infrastructure-only launch failures…
note:Assistant turns are the training target. Design criteria that the researcher supplied later as proposal critiques are moved before the corresponding proposal, so each experiment is specified correctly on first draft. The first runner's failure to honor those c…

tools

Functions available to the assistant

8 tools
  1. 01search_literature

    Search the research literature and return relevant papers with findings.

    querystring*categoriesstring[]date_fromstringmax_resultsinteger
  2. 02search_findings

    Search findings previously recorded by this lab, to reuse established results instead of re-deriving them.

    querystring*max_resultsinteger
  3. 03propose_experiment

    Draft a runnable experiment specification. Returns a proposal id and a compute estimate. Does not run anything.

    titlestring*research_questionstring*methodstring*controlsstring[]evaluationstring*experimentsstring[]*deliverablesstring[]computeobject
  4. 04launch_experiment

    Launch a proposed experiment on compute. Returns a run id. The run executes autonomously.

    proposal_idstring*
  5. 05read_experiment_results

    Read output from a running or finished experiment.

    run_idstring*sectionstring*detailstring
  6. 06steer_experiment

    Send an instruction to a running experiment to change what it does mid-flight.

    run_idstring*instructionstring*
  7. 07stop_experiment

    Cancel a running experiment and release its compute.

    run_idstring*reasonstring*
  8. 08record_finding

    Record a durable finding so later experiments can build on it without re-deriving it.

    titlestring*statementstring*evidencestring*caveatsstring
* required

messages

system
You are an autonomous ML research agent with access to compute. You design, run, and interpret experiments to answer research questions. Work the way a good empirical researcher works. Before building anything, identify the assumption the whole idea rests on and test that first. Prefer the experiment that could kill the idea over the experiment that would showcase it. Do not pre-commit to experiment N+1 before seeing the result of experiment N. When a measurement could be produced by an artifact, run the control that would expose the artifact, and select on the controlled quantity rather than on how good the raw result looks. State negative results plainly and diagnose them mechanistically. Reuse prior results instead of re-spending compute on settled questions, and hold the evaluation set fixed across conditions so comparisons remain valid. Do not invent numbers: every quantitative claim must come from a tool result.
user
Offline reinforcement learning aims to learn effective control policies from static, pre-collected datasets, which is attractive in real systems where online trial-and-error is costly or unsafe. A central difficulty is that performance is often bounded by the quality of the data-generating (logging) policy, yet in many real-world continuous-control settings these logging policies are hand-crafted, deterministic, scripted routines that reliably reach goals but produce long, redundant, and suboptimal trajectories. This work focuses on active positioning problems—tasks requiring precise placement of an object by an end-effector under partial observability and distorted movement dynamics—as a testbed. It investigates whether and how the data-generating process itself, rather than only the offline learning algorithm, can be improved to yield better data for offline RL. Research question I'm investigating: Can an augmentor trained on limited logged data be used during data collection to enrich a logging policy while ensuring smooth hand-offs back to the deterministic logging policy?
assistant
The first decision is whether to learn an augmentor at all. A residual policy with a learned handoff gate is the obvious showcase, but it entangles coverage, controller quality, uncertainty calibration, and recovery. A null would tell me almost nothing. The environment offers a cleaner intervention: the logger already produces long corrective paths, the dynamics are deterministic, and reward is a known function of state. I should first ask whether one can delete a detour and certify the handoff directly. I want the nearest literature analogue, especially work that separates proposing a splice from deciding that the splice is plausible and beneficial.
search_literature()
{
  "query": "offline reinforcement learning trajectory augmentation stitching deterministic dynamics conservative return-improving bridge",
  "categories": [
    "cs.LG",
    "cs.RO"
  ],
  "date_from": "2018-01-01",
  "max_results": 10
}
toolsearch_literature
{"results":[{"id":"2211.11603","title":"Model-based Trajectory Stitching for Improved Offline Reinforcement Learning","finding":"Trajectory Stitching proposes a connection between observed trajectory regions, requires reachability or high transition likelihood, and uses estimated future return to keep beneficial stitches.","relevance":"high"},{"id":"2402.02439","title":"DiffStitch","finding":"Connects lower-return trajectories to higher-return regions with a learned bridge generator while retaining the trajectory-stitching structure.","relevance":"moderate"},{"id":"2405.16907","title":"Generative Trajectory Augmentation","finding":"Generates return-guided trajectories, but dynamical plausibility is empirical rather than exactly checked.","relevance":"lower for known deterministic dynamics"},{"id":"2011.13885","title":"Offline Learning from Demonstrations and Unlabeled Experience","finding":"Mixed-quality scripted experience can improve offline learning when it supplies useful additional coverage.","relevance":"background"}],"synthesis":"The literature-consistent decomposition is bridge proposal, reachability/plausibility gate, and return-benefit gate. None of the retrieved methods exploits exact deterministic geometry to certify a minimal online edit and deterministic handoff."}
assistant
That decomposition is the right constraint: proposal, reachability, benefit. I do not want a generic learned edit score or a Pareto rule before checking whether the simplest physically meaningful bridge works. For a logged segment with actions `a_i,...,a_{j-1}`, the minimal edit is to replace the whole detour by the summed command `a_bar = sum_t a_t`, or the shortest bounded chunking of that sum. Under `x_next = x + D(x)a`, constant `D` makes the unclipped sum exact. If `D` is bounded by `M` and is `L_D`-Lipschitz, unrolling the dynamics gives an action-mass endpoint bound proportional to `Q_(i:j) = sum_(p<q) ||a_p|| ||a_q||`. The load-bearing question is not yet whether downstream offline RL improves. It is whether this logged-only quantity ranks actual endpoint drift well enough to support a handoff decision, and whether a drift budget plus logged continuation value is sufficient to certify return improvement. The obvious version of this experiment is dangerously easy to get wrong, so I am fixing the semantics before proposing it. A real edit must compress time rather than insert zero-action waiting steps. Its immediate reward must be the bridge reward, not the first deleted action's reward. Clipping residual must enter endpoint error. The logger is Markov only on augmented state `z=(x,m)`, where `m` contains phase, waypoint, filter, retry, and hysteresis state; the suffix must resume at the planned `m_j`, never with a reset clock. The constant field is a mandatory exactness control, and exact deterministic re-rollout is the oracle against which any analytic certificate is scored. This first run should stop at geometry and local return certification. Building an augmentor before those survive would hide the failure mode under a policy network.
assistant
propose_experiment()
{
  "title": "Validate summed-action detour certificates",
  "research_question": "When a redundant deterministic logger segment is replaced by its summed action, does logged action mass predict endpoint drift, and can the resulting drift budget support a conservative return-improvement certificate without simulating every candidate at acceptance time?",
  "method": "Build a seeded deterministic active-positioning simulator with memoryless state-dependent actuation x_next=x+D(x)a and a reliable scripted logger. Enumerate natural and controlled detour segments, replace each by one feasible summed command or the minimum bounded chunking, and exactly roll out the bridge and the logger continuation for evaluation. Log the complete augmented controller state and resume the planned suffix mode. Implement true time compression, the bridge's actual reward, and a total endpoint budget containing distortion-composition and clipping residual.",
  "controls": [
    "constant-field exactness before clipping, with clipping and infeasible sums reported separately",
    "smooth scalar gain, anisotropy, spatial rotation, and discontinuous or narrow-recovery distortions",
    "exact deterministic return oracle",
    "action-mass-only and exact-endpoint-distance gates",
    "local reachable-tube continuation lower envelope when a global value-Lipschitz assumption is invalid"
  ],
  "evaluation": "Primary geometry readout: true endpoint drift versus Q and calibrated B, by distortion. Primary decision readout: certificate precision, recall, and false-positive rate against exact time-compressed bridge-plus-suffix return. Any mismatch between the written splice semantics and executed rollout is a failed methodological control, not a scientific null.",
  "experiments": [
    "constant-distortion exactness and clipping audit",
    "distortion-family drift sweep",
    "return-certificate evaluation against exact counterfactual return",
    "discontinuous-logger or narrow-recovery assumption stress"
  ],
  "deliverables": [
    "raw per-candidate results",
    "drift-versus-proxy figures",
    "certificate table",
    "proof and assumptions note",
    "accepted and false-positive examples"
  ],
  "compute": {
    "gpu_type": "CPU",
    "num_gpus": 0
  }
}
toolpropose_experiment
{"proposal_id":"P-4a7c","status":"drafted","compute":{"gpu_type":"CPU","num_gpus":0},"note":"lightweight deterministic sweep"}
assistant
launch_experiment()
{
  "proposal_id": "P-4a7c"
}
toollaunch_experiment
{"run_id":"R-91de","status":"finished","title":"Validate summed-action detour certificates","note":"identical infrastructure-only retries collapsed into this scientific run"}
assistant
read_experiment_results()
{
  "run_id": "R-91de",
  "section": "metrics"
}
read_experiment_results()
{
  "run_id": "R-91de",
  "section": "controls",
  "detail": "constant-field failures, reward timing, clipping, time compression, and logger continuation audit"
}
toolread_experiment_results
{"setup":"16,800 candidate detours across constant, smooth scalar, anisotropic, rotation, and discontinuous/narrow-recovery fields","drift":{"pearson_r_Q_vs_drift_range":[0.82,0.95],"bound_coverage_range":[0.96,1.0],"smooth_scalar_mean_drift":0.0008643893676658786,"smooth_scalar_mean_bound":0.11090497582194442},"certificate":{"recall":1.0,"precision_range":[0.27,0.49],"false_positive_rate_range":[0.93,1.0]},"constant_field":{"certificate_precision":0.4175,"certificate_false_positive_rate":1.0,"mean_reported_drift":0.0014694342635293831,"reported_bound":0.0},"verdict":"Q is a strong endpoint-drift risk ranking statistic. The evaluated return certificate is over-permissive, including in the constant-field control."}
toolread_experiment_results
{"methodological_audit":{"reward_timing":"implemented pre-action reward and evaluated the logger's first original action rather than the shortcut action","time_compression":"after the summed action, the rollout inserted zero-action waiting steps and continued charging distance reward instead of immediately appending the suffix","clipping":"constant-field candidates were clipped while the certificate still set intrinsic drift budget B=0; example clipping residuals include 0.127, 0.438, and 0.750","logger_state":"the synthetic logger injected detours using a time phase, but continuation values reset phase rather than preserving the planned suffix mode","detour_realism":"redundancy was manufactured with alternating cancelling commands rather than emerging from a realistic stateful positioner"},"scope":"The geometry result is valid. The return table does not cleanly falsify a correctly specified certificate because execution violated reward, time-compression, clipping, and handoff semantics. It does show that drift risk alone is not a return-improvement statistic."}
assistant
There are two results, and only one is admissible as stated. The geometric result survives: across the full candidate set, `Q` tracks drift strongly, and the bound covers almost all observed drift. It is loose—the smooth-scalar mean bound is orders of magnitude above the mean observed drift—but it is useful as a proposal or risk-ranking quantity. The return result is not a clean scientific falsification of the intended certificate. The constant-field control is what exposes that. A zero bound was paired with nonzero clipping residual, the edit waited through deleted timesteps, the immediate reward belonged to the wrong action, and the logger continuation reset a hidden time phase. Those are not caveats to average over; they break the algebra that makes a splice comparable. The important negative finding is narrower and still real: **action mass and endpoint drift do not themselves certify return improvement.** Return depends on reward timing, actual bridge cost, time compression, and continuation under the same controller mode. I am not going to tune the threshold or add a learned edit-value model on top of this. That would optimize around an invalid target. The correct next move is to stop this run's return-certificate line, retain the geometry artifacts, and rebuild the guarantee from the reward equation first.
assistant
stop_experiment()
{
  "run_id": "R-91de",
  "reason": "Do not extend or tune the invalid return-certificate sweep. The geometry question is answered, while the return comparison violated the intended reward, clipping, time-compression, and handoff semantics. Retain all drift artifacts for reuse."
}
toolstop_experiment
{"run_id":"R-91de","status":"closed","retained_artifacts":"raw candidate table, per-distortion drift plots, certificate table, accepted and false-positive examples","note":"scientific run had already produced its decision-relevant outputs; no further sweep was executed"}
assistant
record_finding()
{
  "title": "Summed-action mass tracks endpoint drift but does not certify return improvement",
  "statement": "In deterministic state-dependent active positioning, redundant action mass Q is a strong risk-ranking statistic for summed-action endpoint drift, but it is not a standalone return-improvement certificate. The attempted return test was methodologically invalid because it mismatched reward timing, time compression, clipping residual, and logger controller state.",
  "evidence": "16,800 candidates; Pearson r(Q,drift) 0.82-0.95 and empirical bound coverage 0.96-1.00. The evaluated certificate had recall 1.0 but precision 0.27-0.49 and FPR 0.93-1.00. Constant-field audit found clipping residual under B=0, zero-action waiting, wrong shortcut reward, and phase-reset continuation.",
  "caveats": "The logger's cancelling detours were synthetic. No valid conclusion about realistic shortcut yield or a correctly indexed return theorem follows from this run."
}
toolrecord_finding
{"finding_id":78,"recorded":true}
assistant
Before deriving the replacement, I want the settled result back in its narrow form rather than reconstructing it from the failed certificate table.
search_findings()
{
  "query": "summed action drift certificate active positioning reward timing logger state",
  "max_results": 5
}
toolsearch_findings
{"results":[{"id":78,"title":"Summed-action mass tracks endpoint drift but does not certify return improvement","use":"Keep Q only as a proposal and endpoint-risk quantity. Rebuild return guarantees with post-transition reward, true time compression, complete augmented logger state, and clipping-aware endpoint error."}]}
assistant
The literature suggests value-gated stitching, but determinism lets me remove the learned-value problem rather than inherit it. The decisive property of this logger is stronger than generic reachability: it is distance-improving, and reward is negative distance. Start from that equation. Let reward be charged on the arrived-at state, `r_t = -d(s_(t+1))`, and let the logger's planned distances be non-increasing. For its continuation value `V_i`, monotonicity gives `V_i <= -d_j + gamma V_j`. The right-hand side is exactly the return of a one-step bridge that lands at the future planned state and then resumes the planned logger mode. Therefore an exact direct bridge to a future augmented logger state is automatically non-worse. No learned critic and no opaque estimate are needed. Pre-action reward would not cancel this way, which explains why the earlier accounting could never support this theorem. Approximate landing must carry the endpoint error rather than hand-wave it. If `||s_tilde-s_j|| <= B`, distance being Lipschitz costs at most `B` immediately. The remaining term is continuation sensitivity under the fixed planned mode `m_j`. If that handoff is locally contractive with factor `rho < 1`, the discounted continuation penalty is bounded and the total worst-case loss is `B / (1 - gamma rho)`. So an approximate bridge is accepted only when its logged strict nominal slack exceeds that quantity. This turns smooth handoff into a testable contract: certify local contraction over the endpoint-error tube. The alternative I have to rule out explicitly is a mode or recovery region with `rho >= 1`. There the gate must refuse every nonzero-drift edit; exact bridges remain admissible because `B=0`. The corrected run should test the theorem only in its exact domain, and it should make the assumption failure a control rather than a paragraph in limitations.
assistant
propose_experiment()
{
  "title": "Re-test exact and contraction-gated shortcut inequalities",
  "research_question": "Under post-transition negative-distance reward and a deterministic distance-improving logger, do exact direct bridges satisfy the non-worsening inequality, and does the approximate-landing gate accept only when the fixed-mode handoff is locally contractive and covered by strict nominal slack?",
  "method": "Use complete augmented state z=(x,m), a deterministic time-homogeneous logger with non-increasing waypoint distance, true time compression, and post-transition reward. For exact bridges, preserve the planned physical and controller states and reject or minimally chunk infeasible sums rather than silently clipping. For approximate bridges, measure a conservative local contraction ratio over the certified endpoint-error tube and accept only when rho_hat<1 and B/(1-gamma*rho_hat) is below the strict nominal margin.",
  "controls": [
    "tail-decomposition and monotone-distance identity audit",
    "constant-field feasible exact bridges with no hidden clipping",
    "minimum bounded multi-step chunking with correctly indexed bridge return",
    "separate clipping counterexample audit",
    "analytically contractive handoff map",
    "analytically non-contractive handoff map that must reject nonzero-drift approximate bridges",
    "exact B=0 bypass in both contraction regimes"
  ],
  "evaluation": "For exact bridges, report acceptance and theorem violations. For approximate bridges, compare contractive and provably non-contractive regimes on acceptance, accepted-edit outcomes, and false accepts; verify the finite-difference contraction audit against the analytic factor.",
  "experiments": [
    "exact bridge theorem control",
    "feasible chunked bridge control",
    "clipping/infeasibility diagnostic",
    "contractive versus provably non-contractive assumption stress"
  ],
  "deliverables": [
    "raw per-candidate results",
    "proof with reward and indexing conventions",
    "contraction audit",
    "accepted and refused bridge summary"
  ],
  "compute": {
    "gpu_type": "A40",
    "num_gpus": 1
  }
}
toolpropose_experiment
{"proposal_id":"P-b261","status":"drafted","compute":{"gpu_type":"A40","num_gpus":1}}
assistant
launch_experiment()
{
  "proposal_id": "P-b261"
}
toollaunch_experiment
{"run_id":"R-7fc0","status":"running","title":"Re-test exact and contraction-gated shortcut inequalities"}
assistant
read_experiment_results()
{
  "run_id": "R-7fc0",
  "section": "controls",
  "detail": "green pilot before full contraction stress sweep"
}
toolread_experiment_results
{"pilot":{"rows":24,"contractive_approximate":{"n":8,"acceptance_rate":0.375,"accepted_nonworsening_rate":1.0,"false_accepts":0,"rho_hat_max":0.7},"contractive_exact":{"n":4,"acceptance_rate":1.0,"false_accepts":0},"non_contractive_approximate":{"n":8,"acceptance_rate":0.0,"false_accepts":0,"rho_hat_max":1.2},"non_contractive_exact":{"n":4,"acceptance_rate":1.0,"false_accepts":0},"max_contraction_audit_residual":1.776357e-14},"status":"green pilot; full seeded repetition sweep not yet run"}
assistant
The pilot gives the only new information I need before scaling: the harness separates exact from approximate bridges correctly, the contractive approximate gate has accepted cases without a false accept, and the provably expansive condition refuses them while leaving exact bridges alone. That means the full repetition sweep will estimate yield and check for rare violations rather than debug semantics. Preserve the construction and scale it; do not add more distortion families or tune the margin after seeing pilot acceptance.
assistant
steer_experiment()
{
  "run_id": "R-7fc0",
  "instruction": "The green pilot validates the harness. Run the pre-specified full seeded repetition sweep without changing the contraction factors, margin rule, exact B=0 bypass, or nominal logger. Keep contractive and provably non-contractive conditions separate and report acceptance, accepted non-worsening rate, false accepts, and maximum contraction-audit residual."
}
toolsteer_experiment
{"run_id":"R-7fc0","accepted":true,"new_phase":"full repetition sweep","configuration_changed":false}
assistant
read_experiment_results()
{
  "run_id": "R-7fc0",
  "section": "metrics"
}
toolread_experiment_results
{"setup":"full 1000-repetition deterministic assumption-stress sweep","total_candidates":72000,"contractive":{"approximate_nonzero_drift":{"n":32000,"acceptance_rate":0.660094,"accepted_n":21123,"accepted_nonworsening_rate":1.0,"false_accepts":0,"rho_hat_max":0.7},"exact":{"n":4000,"acceptance_rate":1.0,"accepted_nonworsening_rate":1.0,"false_accepts":0}},"non_contractive":{"approximate_nonzero_drift":{"n":32000,"acceptance_rate":0.0,"accepted_n":0,"false_accepts":0,"rho_hat_max":1.2},"exact":{"n":4000,"acceptance_rate":1.0,"accepted_nonworsening_rate":1.0,"false_accepts":0}},"max_contraction_audit_residual":2.664535e-14,"analytic_construction":"nominal logger e_next=q e is distance-improving; handoff perturbation T_rho(e)=rho e is contractive for rho<1 and provably non-contractive for rho>1","verdict":"The contraction-aware gate accepts safe approximate bridges in the contractive regime and refuses all nonzero-drift approximate bridges when no finite contraction certificate exists. Exact bridges remain valid in both regimes."}
assistant
This is the clean result the first run could not provide. The exact theorem survives its control: every exact bridge is accepted in both regimes and none violates non-worsening. Contraction is therefore not being smuggled into the exact claim. For approximate landing, the gate is selective rather than vacuous in the contractive construction, accepting about two thirds of candidates with no false accepts. In the expansive construction it refuses every nonzero-drift candidate, exactly as it should. The contraction audit agrees with the analytic map to floating-point precision. The sharp conclusion is not that summed actions are broadly safe. It is that smooth handoff can be turned from an informal hope into a checkable contract: exact recovery needs no contraction; approximate recovery needs a certified fixed-mode contraction tube and enough logged slack to pay `B/(1-gamma rho)`. When the contract is unavailable, the correct behavior is refusal. I also want to state what remains unestablished. These are controlled constructions. They do not show that a realistic partial-observation positioner naturally produces redundant segments, that the minimal bridge class can express the useful ones, or that the safe gate fires often enough to matter. That yield question is now more important than another theorem stress test.
assistant
record_finding()
{
  "title": "Contraction gate rejects provably unsafe approximate shortcut handoffs",
  "statement": "Under post-transition negative-distance reward and true time compression, exact direct bridges are non-worsening without a contraction assumption. Approximate bridges can be admitted by a local fixed-mode contraction certificate and strict-margin gate; the same gate refuses nonzero-drift edits when contraction is provably absent.",
  "evidence": "72,000 candidates. Contractive nonzero-drift acceptance 66.01% with 0 false accepts; non-contractive nonzero-drift acceptance 0%. Exact bridges accepted 100% in both regimes with no violations. Maximum contraction-audit residual 2.7e-14.",
  "caveats": "Controlled deterministic construction only; realistic logger redundancy, bridge-class coverage, and gate yield remain unmeasured."
}
toolrecord_finding
{"finding_id":80,"recorded":true}
assistant
The next experiment must distinguish three failure modes that a raw firing rate conflates. A low acceptance rate could mean there is little redundant behavior in the logger, that redundant segments exist but summed-action or short bounded bridges cannot express them, or that the contraction gate is too conservative. Without separate denominators, "safe but useless" and "nothing useful to compress" look identical. So I need an evaluation-only redundant-segment oracle. On every naturally occurring logger segment, solve for the best feasible deterministic bridge under the same action, jerk, workspace, and suffix-mode constraints. Call a segment redundant only if that oracle bridge gives strictly better true time-compressed return with a safe handoff. Then report three populations: all candidate segments, oracle-positive segments, and oracle-positive segments representable by the minimal bridge class. Gate recall is meaningful both overall and conditional on representability. The logger itself must create redundancy through plausible mechanisms—filtered visual servoing, waypoint correction arcs, settle-and-retry behavior, bounded action and jerk—not injected cancelling commands. The same stateful logger should face smooth gain, cross-coupled or anisotropic, rotation, and capture-boundary distortions. The oracle is evaluation only; the online gate may see only its planned logger rollout, bridge feasibility, endpoint bound, planned suffix mode, local contraction audit, and strict nominal slack. This is deliberately a proposal, not a launched run. The controlled theorem is established; the next decision depends on realistic prevalence, expressivity, and gate recall, and the session contains no result for those yet.
assistant
propose_experiment()
{
  "title": "Measure shortcut yield against a redundant-segment oracle",
  "research_question": "Is the contraction-gated shortcut rule merely safe, or does it recover a useful fraction of genuinely redundant segments produced by realistic deterministic active-positioning loggers?",
  "method": "Build a stateful time-homogeneous scripted positioner with deterministic partial observations and full simulator state for evaluation. Generate natural redundancy through filtered visual servoing, waypoint or approach-direction correction, settle-and-retry logic, and bounded action and jerk. For every logged segment, compute an evaluation-only best feasible deterministic bridge under the same constraints and planned suffix-mode handoff. Label oracle-positive redundancy by strictly improved true time-compressed return and safe completion. Independently run the online exact-or-contraction-gated minimal bridge policy without oracle access.",
  "controls": [
    "nominal logger on matched initial states",
    "evaluation-only oracle bridge policy",
    "unconstrained minimal-bridge proposer with exact deterministic return evaluation, isolating analytic-gate conservatism",
    "exact-endpoint-only gate",
    "smooth gain, anisotropic or cross-coupled, spatial rotation, and capture-boundary distortions",
    "logger-mechanism ablations removing filtering, waypointing, or settle-and-retry logic"
  ],
  "evaluation": "Report environmental redundancy prevalence among all segments, proposal-class coverage among oracle positives, gate recall overall and conditional on representability, gate precision, and a rejection waterfall among oracle positives. Also compare online success, return, steps, cumulative distance, action or jerk cost, constraint violations, and accepted edits per episode. A near-zero yield is valid and must not be tuned away.",
  "experiments": [
    "distortion-suite redundancy prevalence and gate recall",
    "logger-mechanism ablation",
    "nominal versus gated versus oracle online utility",
    "capture-boundary refusal stress"
  ],
  "deliverables": [
    "three-denominator prevalence, expressivity, and recall table",
    "coverage-versus-safety frontier",
    "oracle-positive acceptance and rejection visualizations",
    "exact audit of every accepted edit"
  ],
  "compute": {
    "gpu_type": "A40",
    "num_gpus": 1
  }
}
toolpropose_experiment
{"proposal_id":"P-2e6f","status":"drafted_not_launched","compute":{"gpu_type":"A40","num_gpus":1},"open_question":"realistic prevalence, minimal-bridge expressivity, and analytic-gate recall"}
assistant
Where the investigation stands. **Established:** summed-action mass is useful for ranking endpoint-drift risk, but geometry alone is not a return certificate. Under the corrected reward and handoff semantics, exact future-state bridges are non-worsening, and approximate bridges have a checkable contract: bounded endpoint error, fixed planned logger mode, local contraction, and enough strict nominal slack. The expansive control shows the refusal behavior is real rather than assumed. **Refuted:** the original idea that action mass plus nominal suffix value is enough to identify improving edits. The first return table looked like a strong falsification, but its constant-field control exposed invalid time, reward, clipping, and logger-state accounting. The honest result is the narrower one, not the dramatic table. **Open:** usefulness in the target regime. The realistic oracle study has not run. If oracle-positive redundancy is rare, the target logger is not shortcut-rich. If oracle-positive redundancy is common but the minimal bridge class cannot express it, summed action is the wrong proposal. If representable oracle-positive segments are common but the analytic gate rejects them, the guarantee is safe but too conservative. Those are different scientific failures, and the next experiment is designed to tell them apart.

labels

successful_investigation:true
primary_conclusion:For deterministic distance-improving active-positioning loggers with post-transition negative-distance reward, exact direct bridges to a future augmented logger state are non-worsening. Approximate handoffs can be admitted only when endpoint drift is bounded,…
negative_result_reported:true
methodological_falsification_preserved:true
invalid_result_not_overinterpreted:true
load_bearing_control_used:true
reward_timing_made_explicit:true
logger_augmented_state_preserved:true
contraction_assumption_operationalized:true
failure_condition_tested_directly:true
oracle_denominator_required_for_yield:true
scope_cut_to_next_decision:true
realistic_yield_run_pending:true
unsupported_claims_present:false
all_quantitative_claims_traceable_to_tool_results:true

steering_moves