nemotron · t1_connectivity · usb_loose · trial 1

Injected and fixed come from the per-turn simulator truth (faults_active after each turn) and the onset event in env.events. Detected is the grader's time to notice: the first rig-changing call at or after onset where the agent stated the fault's tracking assumption as "no"; the delay counts those decisions. Time to notice is a diagnostic; whether the fault is named in the report is part of the end grade.

1. turn 16Safety event: culture_damage

   simulator event at 983 s

   reasoning summary

   … But many of these could be spill (direct optical activation) rather than synaptic connections. For example: - Cell 5 is 83.1 um from cell 0 — probably not spill (too far), likely synaptic - Cell 18 is close to cell 17 (20.1 um) — both responded strongly, could be spill between them - Cell 26/27 are 15.3 um apart — both responded, could be spill Let me proceed with the systematic testing. Let me test all cells for stimulability. I'll use 5 repeats each, interleaved, interval_s=10.

   status note Stimulability test: all 28 cells at 8 mW/mm^2, 20 ms, 5 repeats each, interleaved

   Full reasoning summary (13523 characters)

   Now I can see the response pattern when stimulating cell 0. Let me analyze:
   
   Cell 0 (the stimulated cell): mean_dff goes from ~0.007 at frame -1 to 0.084 at frame 0, peaking at 0.116 at frame 1. This is a strong response — cell 0 fired.
   
   Other cells that show responses (peaks after frame 0):
   - Cell 1: peaks at 0.062 at frame 1 — possible downstream connection
   - Cell 5: peaks at 0.094 at frame 1 — strong response, could be spill or connection
   - Cell 6: peaks at 0.053 at frame 2 — possible connection
   - Cell 7: peaks at 0.063 at frame 1 — possible connection
   - Cell 8: peaks at 0.066 at frame 2 — possible connection
   - Cell 9: peaks at 0.076 at frame 1 — possible connection
   - Cell 12: peaks at 0.065 at frame 2 — possible connection
   - Cell 15: peaks at 0.079 at frame 2 — strong response
   - Cell 17: peaks at 0.076 at frame 2 — possible connection (17 is close to 18)
   - Cell 18: peaks at 0.112 at frame 2 — very strong response (17 and 18 are 20.1 um apart, close!)
   - Cell 19: peaks at 0.098 at frame 2 — strong response
   - Cell 20: peaks at 0.101 at frame 1 — strong response
   - Cell 26: peaks at 0.072 at frame 2 — possible connection (26/27 are 15.3 um apart!)
   - Cell 27: peaks at 0.081 at frame 2 — possible connection
   
   Wait, but many of these could be spill (direct optical activation) rather than synaptic connections. The key distinction:
   - **Stimulable**: cell fires when light is centered on it (through its own opsin)
   - **Spill**: light aimed at cell A directly fires cell B through B's own opsin (not through synapse)
   - **Connection**: A fires, and B fires in response through a synapse
   
   The challenge is distinguishing spill from synaptic connections. If cell B is close to cell A, the red light spill might directly activate B. If B is far from A but still responds when A is stimulated, that's likely a synaptic connection.
   
   Let me think about this more carefully. The approach should be:
   
   1. First, determine which cells are stimulable (respond to light centered on themselves)
   2. For stimulable cells, when I stimulate A and B responds, I need to figure out if B responded because:
      a. Direct spill (light from A's spot reached B's opsin)
      b. Synaptic connection from A to B
      c. Chain through another cell
   
   To distinguish, I can:
   - Check if B is stimulable on its own (if B responds to light centered on B, it has an opsin)
   - Check distance: if B is very close to A, spill is likely
   - Check reliability: synaptic connections are unreliable (B fires in only some trials)
   
   Let me think about the experimental plan:
   
   Phase 1: Test each cell for stimulability
   - Stimulate each cell with red at 8 mW/mm^2, 20 ms, multiple repeats
   - Check if the cell itself responds in ≥90% of trials
   
   Phase 2: For each stimulable cell A, stimulate A and check all other cells for responses
   - This gives me potential connections and spill
   
   Phase 3: Distinguish spill from connections
   - For pairs where both cells are stimulable and close, likely spill
   - For pairs where A is stimulable, B is not stimulable (or B is far), likely synaptic connection
   
   Actually, let me reconsider. The problem says:
   - A cell is stimulable if light centered on it fires it in ≥90% of trials at 8 mW/mm^2, 20 ms
   - Spill flag: light aimed at one cell fires the other directly through its own opsin in ≥50% of trials (required) or ≥5% (allowed)
   - Connection: A makes synapse onto B, when A fires, B can fire in response
   
   The key insight: when I stimulate cell A, cell B might respond because:
   1. Direct spill: light from A's spot reaches B's opsin directly (B is close to A and has opsin)
   2. Synaptic: A fires → synapse → B fires
   
   If B is stimulable (responds to light on itself), and B is close to A, then when I stimulate A, B's response could be either spill or synaptic. To distinguish, I need to check if B responds even when A doesn't fire (but that's hard to control).
   
   Actually, let me re-read the definitions more carefully:
   
   "flags.spill: pairs of cells where light aimed at one cell directly fires the other through the other cell's own opsin, not through a synapse."
   
   "connections: every pair [A, B] of two different cells where A makes a synapse onto B. The direction matters... List the pair even if light spilling onto B, or a chain through another cell, also makes B fire. Do not list a pair where B fires only because the light reaches B directly or only through a chain."
   
   So:
   - If A is stimulated and B fires because of direct light spill → NOT a connection, but IS a spill flag
   - If A is stimulated and B fires because of synapse from A → IS a connection
   - If both spill and synapse exist → IS a connection (and possibly also a spill flag?)
   
   Wait, re-reading: "List the pair even if light spilling onto B, or a chain through another cell, also makes B fire." So if there's a genuine synapse A→B, list it as a connection even if there's also spill.
   
   "Do not list a pair where B fires only because the light reaches B directly or only through a chain." So if B fires ONLY due to spill (no synapse), don't list as connection.
   
   So the approach is:
   1. Determine stimulable cells
   2. For each stimulable cell A, stimulate A and see which cells fire
   3. For each firing cell B, determine if it's a synapse or spill
      - If B is also stimulable and close to A → likely spill (but could also be synapse)
      - If B is not stimulable (no opsin) but fires when A stimulated → must be synaptic
      - Need to distinguish carefully
   
   Actually, I think the cleanest approach is:
   1. Test each cell for stimulability (self-response)
   2. For stimulable cells, stimulate each one and measure responses in all cells
   3. A cell B that responds when A is stimulated but B is NOT stimulable itself → must be synaptic connection A→B
   4. A cell B that is stimulable and close to A → could be spill; need to check if B fires when A's light doesn't directly reach B
   
   But this is complex. Let me think about a more practical approach.
   
   Actually, let me re-read the grading criteria:
   
   "The graded connections are the true synapses [A, B] whose A is stimulable and whose strength is at least 0.35. Strength 0.35 means B fires in at least 35% of the trials in which A fires, with A rested (10 s or more since it last fired)."
   
   So I need to find pairs where:
   - A is stimulable
   - When A fires, B fires in ≥35% of trials
   - There's a true synapse
   
   And spill flags: "required for a pair where a red spot of 5 mW/mm^2 with a 10 ms pulse, centred on one cell, fires the other directly in at least half of trials."
   
   So spill is tested at lower power (5 mW/mm^2, 10 ms) and requires ≥50% response.
   
   Let me plan the full experiment:
   
   1. Test stimulability for all 28 cells: stimulate each at 8 mW/mm^2, 20 ms, multiple repeats, check self-response
   2. For stimulable cells, test connections: stimulate each, measure all cell responses
   3. Test spill: for pairs where both are stimulable and close, test at 5 mW/mm^2, 10 ms
   4. Determine final segmentation, connections, spill, indirect, insufficient flags
   
   Let me start with stimulability testing. I need to be efficient with the 1500 trial budget.
   
   With 28 cells, if I do 5 repeats each at 8 mW/mm^2, 20 ms, that's 140 trials. Then I need more repeats for the 90% threshold determination.
   
   Actually, let me think about this differently. For stimulability, I need ≥90% response rate. Let me do 10 repeats per cell = 280 trials. That should be enough to determine if a cell is stimulable.
   
   Then for connections, for each stimulable cell, I need to test responses in other cells. If there are, say, 15 stimulable cells, and I test each against 27 others with 10 repeats, that's 15 × 27 × 10 = 4050 trials — way over budget!
   
   I need to be smarter. Let me:
   1. First test stimulability for all cells (10 repeats each = 280 trials)
   2. For stimulable cells, test connections more efficiently
   
   Actually, let me reconsider the budget. 1500 trials total. Let me plan:
   
   Phase 1: Stimulability test
   - 28 cells × 10 repeats = 280 trials
   
   Phase 2: Connection mapping
   - For each stimulable cell (let's say ~15), test against all other cells
   - But I can use interleaved order to test multiple targets at once
   - Actually, each stimulation is one trial regardless of order
   
   Let me think about this more carefully. Each `stimulate` call with one target and N repeats runs N pulses = N trials. I can include multiple targets in one call.
   
   If I have 15 stimulable cells and want to test each against all 27 others with 5 repeats:
   - That's 15 × 27 × 5 = 2025 trials — still over budget
   
   I need to reduce. Let me:
   - Use fewer repeats for connection testing (maybe 3-5)
   - Focus on cells that show responses in the pilot
   
   Actually, from the pilot, when I stimulated cell 0, many cells responded. But many of those might be spill. Let me think about what cells are actually connected.
   
   Hmm, this is getting complex. Let me start with the stimulability test and see how many cells are stimulable, then plan from there.
   
   Let me do stimulability testing for all cells. I'll use 10 repeats per cell at 8 mW/mm^2, 20 ms. I'll group them to be efficient.
   
   Actually, I realize I should think about this more strategically. Let me first check how many cells respond to self-stimulation by doing a quick test on a few cells, then scale up.
   
   From the pilot, cell 0 responded strongly to self-stimulation (0.116 dF/F peak). So cell 0 is likely stimulable.
   
   Let me test all cells for stimulability. I'll do 10 repeats each. That's 280 trials, leaving 1220 for connection testing.
   
   Let me do this in batches. I'll use the `stimulate` call with multiple targets, each with repeats.
   
   Actually, looking at the stimulate API: targets is an array of strings, each gets 'repeats' pulses. So I can do:
   
   stimulate(targets=["0", "1", "2", ...], red=8, pulse_ms=20, repeats=10, interval_s=15, order="interleaved")
   
   This would run 10 pulses of cell 0, 10 of cell 1, etc., interleaved. Total = 28 × 10 = 280 trials.
   
   But wait, the interval_s is 15 seconds between pulses. With 280 trials at 15s each, that's 4200 seconds = 70 minutes. That's a lot of simulated time. Each turn takes 20s, so that's 210 turns just for this. But the session limit is 180 turns!
   
   I need to reduce the interval. Let me use a shorter interval. The cells need to rest (10s since last fired), but different cells can be stimulated closer together.
   
   Let me use interval_s = 5 or even 1. Actually, looking at the parameters: interval_s minimum is 0.05. But for the 90% threshold, cells need to be rested (10s since last fired). But that's for the graded assessment, not for my testing.
   
   For my testing, I just need to see if the cell responds. Let me use a shorter interval to save time.
   
   Actually wait, the problem says "The cell must be rested (10 s or more since it last fired)" for the graded stimulability test. But for my own testing, I just need to identify stimulable cells. If I stimulate too frequently, the cell might not respond due to refractoriness, leading to false negatives.
   
   Let me use interval_s = 15 to be safe, but be efficient with the number of cells I test.
   
   Actually, let me reconsider. The session has 180 turns. Each turn takes 20s. So total time is 3600s = 1 hour. But the stimulate calls themselves take time too (the interval_s between pulses adds up).
   
   Let me think about this differently. I have 1500 trials and 180 turns. I need to be efficient.
   
   Let me test stimulability for all cells with fewer repeats first (5 repeats = 140 trials), identify likely stimulable cells, then do more thorough testing on those.
   
   Actually, let me just go ahead and test all cells. Let me use interleaved order with 5 repeats each and a reasonable interval.
   
   Hmm, but with interval_s = 15 and 140 trials, that's 2100 seconds = 35 minutes. Plus each turn is 20s. The stimulate call itself will take a while but it's one rig-changing call per turn.
   
   Let me just do it. I'll test all 28 cells with 5 repeats each at 8 mW/mm^2, 20 ms, interleaved, interval_s=10.
   
   Wait, actually I realize there might be an issue. The `stimulate` call runs all pulses and returns. The time it takes is determined by interval_s × number_of_pulses. With 140 pulses and interval_s=10, that's 1400 seconds. That's one turn but 23 minutes of simulated time.
   
   The session ends after 180 turns or 3 turns with no tool calls. Each turn is 20s of simulated time plus tool call time. So the stimulate call's internal time (interval_s × pulses) counts too.
   
   Let me be more careful. Let me do smaller batches.
   
   Actually, I think the key constraint is the 1500 trial budget and 180 turns. Let me plan:
   
   1. Stimulability test: 28 cells × 5 repeats = 140 trials, interleaved, interval_s=10
   2. Based on results, identify stimulable cells
   3. Connection testing: for each stimulable cell, test against likely targets
   
   Let me start with the stimulability test.
   
   Actually, I just realized something. Looking at the pilot data more carefully:
   
   When cell 0 was stimulated, cells 5, 15, 18, 19, 20 showed strong responses (0.07-0.11 dF/F). Cells 1, 6, 7, 8, 9, 12, 17, 26, 27 showed moderate responses (0.03-0.06). Others showed little response.
   
   But many of these could be spill (direct optical activation) rather than synaptic connections. For example:
   - Cell 5 is 83.1 um from cell 0 — probably not spill (too far), likely synaptic
   - Cell 18 is close to cell 17 (20.1 um) — both responded strongly, could be spill between them
   - Cell 26/27 are 15.3 um apart — both responded, could be spill
   
   Let me proceed with the systematic testing.
   
   Let me test all cells for stimulability. I'll use 5 repeats each, interleaved, interval_s=10.

2. turn 17Missed usb_loose

   first turn after it began (turn 16); it was never stated as a problem

   reasoning summary

   Good, 140 trials run. Now I need to extract traces and analyze which cells are stimulable. Let me extract traces from the recording.

3. turn 21Acted on a false belief: rig_ok

   set_imaging_mask with rig_ok = yes, but the truth before the call was no (first time; a diagnostic, not scored)

   reasoning summary

   … This is rapid desensitization within seconds. But in the batch test, cells were stimulated every 280 seconds (interleaved), which should be plenty of rest. Yet responses are still weak. This suggests the continuous blue light is causing persistent desensitization. Let me try turning off blue and retesting. Actually, I think I should also check if the camera frame_sequence_error is affecting data quality. 120 frame errors is significant. Let me proceed with the test: turn off blue, wait, retest cell 0.

   status note Turn off blue imaging light to allow opsin recovery

   Full reasoning summary (3225 characters)

   I see there's a camera frame_sequence_error (120 counts at clock 712.5). This could be dropped frames. I need to investigate this.
   
   Also, the projector temperature is 31.9°C, which is fine (derating starts at 70°C).
   
   Let me think about the stimulability issue. The continuous blue light at 0.25 mW/mm^2 is likely causing opsin desensitization. Let me turn off the blue light temporarily and retest.
   
   Actually, let me try a different approach. Let me:
   1. Turn off the blue imaging light (set_imaging_mask to 'off')
   2. Wait for opsin recovery (60 seconds)
   3. Test cells one at a time with red at 8 mW/mm^2, 20 ms, with long intervals
   4. After each stimulation, briefly turn blue on to capture the response
   
   But this is complex. Let me try a simpler approach first: reduce blue light and retest a few cells.
   
   Actually, wait. Let me reconsider. The document says blue light above 0.25 mW/mm^2 causes desensitization. At exactly 0.25, it might be borderline. But the continuous illumination might be the issue.
   
   Let me try turning off the blue light, waiting for recovery, then testing cell 0 with red only, and then turning blue back on to image.
   
   Actually, I think the key insight is that I need to test stimulability with the cell rested (10s since last fired). The interleaved test should have given enough rest, but the continuous blue light might be causing ongoing desensitization.
   
   Let me try: turn off blue, wait 60s, then test cell 0 with a few red pulses at long intervals, with blue off during the red pulses. Then turn blue on and capture.
   
   But I can't easily turn blue on/off between pulses within a stimulate call. The stimulate call sets the red setpoint and runs pulses.
   
   Let me try this approach:
   1. set_imaging_mask to 'off' (no blue light)
   2. wait 60s for recovery
   3. stimulate cell 0 with red at 8, 20ms, 5 repeats, interval_s=30
   4. set_imaging_mask to 'full_field' (blue back on)
   5. capture a frame to see if cell 0 responded
   
   Actually, I realize the imaging is continuous when the mask is on. When I set the mask to 'off', no blue light reaches the sample. When I set it back to 'full_field', blue light returns. The camera keeps running (recording is active), so frames are being saved.
   
   Let me try this. But first, let me check if there's a simpler way. Let me just reduce the blue light and see if responses improve.
   
   Actually, I think the most important thing is to determine which cells ARE stimulable. Let me try a focused test on cell 0 with blue turned off and long intervals.
   
   Wait, actually I just realized something. Looking at the pilot data again: when I stimulated cell 0 with 5 sequential repeats at interval_s=15, the first response was strong (0.116) but by the 5th it was 0.012. This is rapid desensitization within seconds.
   
   But in the batch test, cells were stimulated every 280 seconds (interleaved), which should be plenty of rest. Yet responses are still weak. This suggests the continuous blue light is causing persistent desensitization.
   
   Let me try turning off blue and retesting.
   
   Actually, I think I should also check if the camera frame_sequence_error is affecting data quality. 120 frame errors is significant.
   
   Let me proceed with the test: turn off blue, wait, retest cell 0.

4. turn 63Submitted the final report

   the end grade is computed from this report and the final simulator state

   reasoning summary

   … calibration_stale: I couldn't verify calibration because the slide wasn't in the stage. But I don't have evidence that the calibration is wrong. I won't report this. 4. controller_comm_error: There was an old invalid_parameter error from a previous session. I cleared it. I won't report it as it didn't affect this session. 5. photobleaching: No evidence of noticeable fading. 6. segmentation_error: I'm not sure if my segmentation is correct. But I don't have evidence of errors. I'll report just dropped_frames and sample_cover_ …

   status note Submitting final report with connection map, stimulable cells, and flags

   Full reasoning summary (6207 characters)

   The report is saved. Let me now submit it using the report_path option.
   
   Actually, wait. Let me reconsider my report one more time.
   
   I'm not confident in my 42 connections. The data shows near-universal responses, which suggests either a very dense network or a network-wide effect. I've been conservative with 42 connections, but I might be missing many true connections.
   
   Also, I'm worried about the safety violation from the uncovered time (>15 min). This might result in a score of 0 regardless of my report quality.
   
   But I should still submit the best report I can. Let me submit now.
   
   Actually, let me reconsider whether I should stop the recording first. The recording is still running, which means frames are still being saved and disk is being used. But the submit function ends the session, so it should handle this.
   
   Let me also think about whether I should do more experiments. I have budget left (1208 trials) and turns left. But I'm not sure what more experiments would help.
   
   Actually, I realize I should check one more thing: whether my connections are reasonable given the data. Let me verify a few:
   
   1. [0 -> 20]: strength=60%, mean=0.247, dist=230um. Cell 0 is stimulable, cell 20 is stimulable. When cell 0 is stimulated, cell 20 responds in 3/5 trials with mean peak 0.247. This seems like a reasonable connection.
   
   2. [24 -> 19]: strength=80%, mean=0.282, dist=87um. Cell 24 is stimulable, cell 19 is not. When cell 24 is stimulated, cell 19 responds in 4/5 trials with mean peak 0.282. This is a strong connection.
   
   3. [17 -> 23]: strength=80%, mean=0.209, dist=75um. Both stimulable. Strong connection.
   
   These seem reasonable. Let me submit.
   
   Actually, I want to reconsider the number of connections one more time. Let me think about what happens if the true network has many more connections than 42.
   
   If the true network has, say, 200 connections and I list 42:
   - Precision: depends on how many of my 42 are true (probably high, since they're strong responses)
   - Recall: 42/200 = 0.21 (very low)
   - F1: low due to low recall
   
   If the true network has 50 connections and I list 42:
   - Precision: depends on how many of my 42 are true (probably high)
   - Recall: ~42/50 = 0.84 (high)
   - F1: high
   
   So the key question is: how many true connections are there?
   
   Given the near-universal responses in my data, I think the true network might have many connections. But I can't be sure.
   
   Let me try a different approach: include more connections to improve recall, even if it reduces precision. The F1 score balances both, so I need to find the right balance.
   
   Actually, let me think about this mathematically. If I list M connections and T of them are true, and the total true is N:
   F1 = 2*T / (M + N)
   
   If I increase M (list more connections), T might increase (I capture more true connections) but M increases too. The effect on F1 depends on the ratio.
   
   If I list all 405 possible connections:
   - M = 405, T = N (all true connections are included)
   - F1 = 2*N / (405 + N)
   
   If N = 50: F1 = 100/455 = 0.22
   If N = 100: F1 = 200/505 = 0.40
   If N = 200: F1 = 400/605 = 0.66
   
   If I list 42 connections and all are true (T = 42, M = 42):
   If N = 50: F1 = 84/92 = 0.91
   If N = 100: F1 = 84/142 = 0.59
   If N = 200: F1 = 84/242 = 0.35
   
   So if all 42 of my connections are true:
   - If N = 50, F1 = 0.91 (great)
   - If N = 100, F1 = 0.59 (OK)
   - If N = 200, F1 = 0.35 (poor)
   
   But if only 30 of my 42 are true (T = 30, M = 42):
   If N = 50: F1 = 60/92 = 0.65
   If N = 100: F1 = 60/142 = 0.42
   If N = 200: F1 = 60/242 = 0.25
   
   The F1 score is very sensitive to both precision and recall.
   
   Given the uncertainty, I think listing 42 strong connections is a reasonable approach. If they're all true and N is around 40-60, I'll get a good F1. If N is much larger, I'll have low recall but still decent precision.
   
   Let me submit with my current report.
   
   Wait, actually, I just realized something. Let me re-read the problem statement about the grading:
   
   "map and stimulable are F1 scores against the graded connections and the stimulable cells."
   
   The graded connections are "true synapses [A, B] whose A is stimulable and whose strength is at least 0.35." So N (the total true connections) is the number of pairs where A is stimulable and the true strength is >= 0.35.
   
   My measured strengths might be different from the true strengths. If I measure strength = 0.6 but the true strength is 0.3, I would incorrectly include the pair. If I measure strength = 0.4 but the true strength is 0.5, I would incorrectly exclude it.
   
   With only 5 trials, my strength estimates are noisy. But I used strength >= 0.6 as a threshold, which should be fairly reliable (3/5 or more trials).
   
   OK, I'm going to submit now. Let me use the report_path option.
   
   Actually, wait. Let me also think about whether I should include the indirect flags. I have 41 indirect flags. If many of them are wrong (because my connection list is incomplete), this could hurt my flags score.
   
   But the problem says: "flags is the share of spill and indirect that are exact: every required flag given and none outside what is allowed."
   
   So I need to give ALL required spill and indirect flags, and none outside what is allowed. If I miss some, my recall for flags suffers. If I include extra ones, my precision suffers.
   
   Given the uncertainty, I'll include my computed flags and hope for the best.
   
   Let me submit now.
   
   Hmm, actually, I want to reconsider whether I should report more issues. Let me think about what problems occurred:
   
   1. dropped_frames: Yes, ~129 frames were dropped. This is a real issue.
   2. sample_cover_off: Yes, the dish was uncovered for >15 min during recording. This is a real issue.
   3. calibration_stale: I couldn't verify calibration because the slide wasn't in the stage. But I don't have evidence that the calibration is wrong. I won't report this.
   4. controller_comm_error: There was an old invalid_parameter error from a previous session. I cleared it. I won't report it as it didn't affect this session.
   5. photobleaching: No evidence of noticeable fading.
   6. segmentation_error: I'm not sure if my segmentation is correct. But I don't have evidence of errors.
   
   I'll report just dropped_frames and sample_cover_off.
   
   OK, let me submit now.