[Bug] Gemini compaction uses unsupported MINIMAL thinking level and ends in context overflow

[Dusan2709]

Summary

Automatic context compaction fails for a Google Gemini session because the compaction request reaches the provider with thinking level MINIMAL, even though the routed model profile declares only high as supported. DSH logs the compaction failure and continues the turn with the uncompressed history until the session terminates with CONTEXT_WINDOW_EXCEEDED.

Reproduction

This occurred in a real SDK-driven, tool-heavy implementation session with the following parameters:

• macOS 26.5 (arm64)
• Node.js 24.18.0
• @deepseek-ai/dsh: 0.1.2-rc.1
• @deepseek-ai/dsh-compaction-basic: 0.1.2-rc.1
• @deepseek-ai/dsh-llm-pi-ai: 0.1.2-rc.1
• @earendil-works/pi-ai: 0.84.4
• provider: google
• model: gemini-3.8-flash
• conversation reasoning effort: high
• context window: 262,144 tokens
• requested maximum output: 49,152 tokens
• automatic compaction: enabled, default threshold/retention policy

The materialized model entry was equivalent to:

{
  "id": "gemini-3.8-flash",
  "reasoningEfforts": {
    "high": "high"
  }
}

The session performed a long implementation turn with many tool calls. When automatic pressure compaction ran, every summary attempt ended with:

HTTP 400 INVALID_ARGUMENT
Thinking level MINIMAL is not supported for this model. Please retry with other thinking level.

The first model turn later completed and produced a long final response. A second turn was requested to correct the output format. During that turn, the model emitted:

pi-ai detected context overflow for model "gemini-3.8-flash"
code: CONTEXT_WINDOW_EXCEEDED

The context projection at termination was:

surfaceTokens:        171972
sampledSurfaceTokens: 171972
pressureTokens:       262780
contextWindow:        262144

The session recorded multiple compaction/start / compaction/end pairs, but every compaction/end contained the same MINIMAL rejection. The final overflow-triggered compaction attempt failed the same way, and the turn ended with CONTEXT_WINDOW_EXCEEDED.

Current behavior

dsh-compaction-basic creates its auxiliary request with purpose: "compaction" but no explicit reasoningEffort. On this route, the resulting provider request uses MINIMAL, which is not supported by the model. The pre-step handler logs the failure and continues, so context keeps growing without a successful replacement checkpoint.

This produces the following failure chain:

context pressure reaches the compaction threshold
  -> automatic compaction starts
  -> provider rejects thinking level MINIMAL
  -> DSH continues with the original uncompressed history
  -> pressure continues to grow
  -> CONTEXT_WINDOW_EXCEEDED
  -> overflow recovery compaction is rejected for the same reason

No credentials, private prompts, repository contents, local paths, or run identifiers are included in this report.

Expected behavior

Compaction should select a reasoning level supported by the exact routed model, or omit the thinking parameter in a provider-compatible way. In this case it should not dispatch MINIMAL when the model profile declares only high.

If no valid compaction reasoning level can be resolved, DSH should surface an actionable compatibility error before repeatedly continuing toward a guaranteed context overflow. A provider/model-specific mapping such as the internal minimal selector to Google's accepted low wire value may also be appropriate when explicitly declared by the route.

It would additionally help if the terminal diagnostic preserved the failed-compaction cause alongside CONTEXT_WINDOW_EXCEEDED, since the overflow is a consequence rather than the initial failure.

[argszero]

Thanks for a report that already points at the right seam. I traced it to the exact lines on both sides, and the shape of your diagnosis is right — but the level that reaches the provider is not "the model's default". On this provider family an omitted reasoning option is a chosen level, and it is one your route declared unsupported.

1. The request that fails is not the one you configured

Harness side — a resolved off is translated into omitting the option (packages/llm/llm-pi-ai/src/adapter.ts:119-123):

const enabledReasoning: ThinkingLevel | undefined = reasoning === 'off' ? undefined : reasoning
return { ...enabledReasoning === undefined ? {} : { reasoning: enabledReasoning }, ... }

The file documents that omission as "the seam's way of saying the capability is unavailable, which leaves the surface offering only the provider's default" (:171-182).

pi-ai side (0.84.4, dist/api/google-generative-ai.js) — for Google that omission is disabled thinking, and disabled thinking is spelled as a level:

// :238-241
if (!options?.reasoning) return stream(model, context, { ...base, thinking: { enabled: false } });

// :341-354
function getDisabledThinkingConfig(model) {
  // Gemini 3.1 Pro cannot disable thinking, and Gemini 3 Flash / Flash-Lite do not
  // support full thinking-off either. For Gemini 3 models, use the lowest supported
  // thinkingLevel without includeThoughts ...
  if (isGemini3ProModel(model))   return { thinkingLevel: "LOW" };
  if (isGemini3FlashModel(model)) return { thinkingLevel: "MINIMAL" };
  if (isGemma4Model(model))       return { thinkingLevel: "MINIMAL" };
  return { thinkingBudget: 0 };   // Gemini 2.x disables properly
}

getDisabledThinkingConfig never reads model.thinkingLevelMap. That map is your declaration: reasoningEfforts: { high: 'high' } pins minimal/low/medium/xhigh/max to null, i.e. unsupported (llm-pi-ai/src/catalog.ts:716-743). So the harness holds the fact that minimal is unsupported and still sends it, because the substitution for "disabled" happens below the layer that knows. The map governs what the harness offers and validates, not what pi-ai substitutes.

2. Why the compaction call is the one that trips

compaction-basic's auxiliary request copies only provider and model out of the routed header, then sets purpose: 'compaction' and nothing else (packages/compaction/compaction-basic/src/summarizer.ts:126-160):

const latest = agent.session.requestHeader()?.config
...
const options: GenerateOptions = { provider: target.provider, model: target.model, ..., purpose: 'compaction' }

requestHeader().config is an LlmCallConfig and carries reasoningEffort too (llm/src/call-config.ts:23-30) — it is the very value your conversation request was built from (agent-loop/src/agent.ts:515-526). The auxiliary call reads that header for routing and drops the effort. It therefore carries a level only when the route declares a reasoning: default; evidently yours does not, so the option is omitted, and on gemini-3.8-flash an omitted option is MINIMAL.

purpose is the documented seat for exactly this policy — "Adapters may map the purpose to model-hidden transport metadata or purpose-specific generation policy" (llm/src/types.ts:453-458) — and llm-deepseek already uses it (llm-deepseek/src/serialize.ts:84: if (options.purpose === 'session-title') return { thinking: 'disabled' }). llm-pi-ai applies no purpose policy at all, so nothing on that route keeps the promise.

3. A second trigger with no compaction involved: selecting "Off"

Same mechanism, wider surface. getSupportedThinkingLevels always includes off, so the picker offers it for this model; selecting it materializes reasoningEffort: 'off' (llm/src/index.ts:879-900), the adapter turns that into an omitted option, and pi-ai turns that into MINIMAL — so every conversation request on this route then fails with the same 400. Compaction is the case you hit; it is not the only one.

4. Why it ends as CONTEXT_WINDOW_EXCEEDED

compaction-basic logs and continues: ctx.logger.warn('step compaction failed: ...; continuing the turn') (compaction-basic/src/index.ts:161-162). Pressure keeps growing; the overflow hook only runs on the overflow code (:184), and its recovery compaction issues the same request, so it is rejected identically. You are right that the terminal diagnostic should preserve the first cause — today it survives only as a warn line.

5. Fix candidates, by layer

• compaction-basic (smallest, targeted). Carry the routed header's reasoningEffort into the auxiliary options, the way provider/model already come from it. The header also records adapterDefaults.reasoningEffort === true when a value was materialized by adapter resolution rather than proposed (llm/src/call-config.ts:32-39, session/src/index.ts:293-312) — that is exactly the discriminator for "propagate it" vs "leave it to resolution".
• llm-pi-ai adapter (policy). Give auxiliary purposes a resolved level instead of an omission, as a purpose === 'compaction' branch alongside the deepseek adapter's session-title one. This also closes case 3, since both are the same request.
• pi-ai (root). getDisabledThinkingConfig should consult thinkingLevelMap — a null entry means unsupported — or "as little thinking as this model allows" needs a spelling other than a level the map denies.
• Diagnostics. Attach the failed compaction's cause to the terminal overflow failure (or append a session event), so the first cause outlives the consequence.

6. A plugin for the recovery path

Since the request cannot be repaired before it is sent from a plugin (the omission is adapter-internal), I built the seam-level half: @argszero/cordis-plugin-thinking-level-fallback@0.1.0 (npm, source).

It wraps the public llm/stream waterfall; when an in-scope call ends in a provider rejection that names a thinking level, it asks llm.resolveModelInfo(provider, model) which levels that exact model declares, picks the nearest one to what the request carried (walking pi-ai's own escalation order upward then downward — the order clampThinkingLevel uses) and re-issues the call once:

compaction call, google/gemini-3.8-flash, declared { off, high }
  attempt 1: no reasoningEffort     -> HTTP 400 "Thinking level MINIMAL is not supported"
  attempt 2: reasoningEffort 'high' -> 200

It is deliberately bounded: auxiliary purposes only by default; never after reader-visible content has streamed; never over the harness' own UNSUPPORTED_REASONING_EFFORT refusal (that one is a misconfigured route and should stay loud); one re-issue, bounded by a process-local marker on the request rather than a counter. The discarded attempt is buffered and never yielded, so the caller sees only the successful stream. 32 tests; verified against @deepseek-ai/dsh-llm@0.1.5-rc.2 and @earendil-works/pi-ai@0.84.4.

It is a stopgap and becomes a no-op once any of the compaction-basic / adapter / pi-ai fixes above land — which is the right outcome: the durable fix belongs where the level is chosen, not where the rejection is observed.

Line numbers are from deepseek-harness master c291e7961a and @earendil-works/pi-ai@0.84.4 as published on npm (fetched and read for this reply), so the pi-ai ones may shift on an upgrade.

[Dusan2709]

Thank you very much for the quick and precise analysis. Tracing where the reasoning effort is dropped, how pi-ai turns the omission into MINIMAL, and the available fix points across the layers is extremely helpful.

I will test @argszero/cordis-plugin-thinking-level-fallback against our pinned DSH 0.1.2-rc.1 setup and the same Gemini route, including a deliberately triggered compaction case.

Thanks again for taking the time to investigate this so thoroughly and for publishing the stopgap so quickly.

[DeGugu-hhh12315]

Same class of bug, hit on a different provider — worth adding because the failure mode is identical and the fix is cheap.

What we saw: a model config carried reasoningEffort: minimal. The endpoint rejected the entire request with HTTP 400 Invalid request parameters (INVALID_REQUEST). That provider's accepted set is only none | low | medium | high — minimal (and xhigh / max) simply are not in its enum. Nothing in the error said which parameter was at fault; it looked like a broken model.

Two details that cost us the most time:

1. The mapping of "off" is not obvious: for that provider, thinking-off must be sent as the literal none. An empty/omitted value does not turn thinking off — it leaves it on, which silently costs tokens on every turn. So a config that "looks off" can be quietly on.
2. A bad effort kills the whole request, so it shows up as "this model is broken" rather than "this knob is out of range".

Suggestions (either would have saved us the debugging):

• Validate the configured effort against the model's declared set at load time — fail loudly at startup (or when the model is selected), not per request;
• When a provider rejects a request because of an unsupported effort, name the field and the accepted values in the surfaced error, instead of a generic 400;
• （optional）treat minimal as a portable alias and map it per provider (minimal → low, or → none if the intent was "no thinking"), rather than passing it through.

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