docs(research): update crisis model quality report (#877)

agent/context_snapshots.py

@@ -1,69 +0,0 @@
-"""First-class context snapshot artifacts for live runtime memory evaluation."""
-
-from __future__ import annotations
-
-import json
-import re
-from pathlib import Path
-from typing import Any
-
-from hermes_constants import get_hermes_home
-
-
-_SAFE_SEGMENT_RE = re.compile(r"[^A-Za-z0-9_.-]+")
-
-
-class ContextSnapshotRecorder:
-    """Write per-call prompt-composition artifacts for a Hermes session."""
-
-    def __init__(self, session_id: str, *, enabled: bool = False, base_dir: str | Path | None = None):
-        self.session_id = session_id or "session"
-        self.enabled = bool(enabled)
-        self.base_dir = Path(base_dir) if base_dir else get_hermes_home() / "reports" / "context_snapshots"
-
-    @property
-    def session_dir(self) -> Path:
-        safe_session = _SAFE_SEGMENT_RE.sub("_", self.session_id).strip("._") or "session"
-        return self.base_dir / safe_session
-
-    def record_call(
-        self,
-        api_call_count: int,
-        *,
-        system_prompt: str,
-        memory_provider_system_prompt: str = "",
-        memory_prefetch_raw: str = "",
-        memory_context_block: str = "",
-        api_user_message: str = "",
-        api_messages: list[dict[str, Any]] | None = None,
-        metadata: dict[str, Any] | None = None,
-    ) -> Path | None:
-        if not self.enabled:
-            return None
-
-        call_dir = self.session_dir / f"call_{api_call_count:03d}"
-        call_dir.mkdir(parents=True, exist_ok=True)
-
-        self._write_text(call_dir / "system_prompt.txt", system_prompt or "")
-        self._write_text(call_dir / "memory_provider_system_prompt.txt", memory_provider_system_prompt or "")
-        self._write_text(call_dir / "memory_prefetch_raw.txt", memory_prefetch_raw or "")
-        self._write_text(call_dir / "memory_context_block.txt", memory_context_block or "")
-        self._write_text(call_dir / "api_user_message.txt", api_user_message or "")
-        self._write_json(call_dir / "api_messages.json", api_messages or [])
-        self._write_json(
-            call_dir / "metadata.json",
-            {
-                "session_id": self.session_id,
-                "api_call_count": api_call_count,
-                **(metadata or {}),
-            },
-        )
-        return call_dir
-
-    @staticmethod
-    def _write_text(path: Path, content: str) -> None:
-        path.write_text(content, encoding="utf-8")
-
-    @staticmethod
-    def _write_json(path: Path, payload: Any) -> None:
-        path.write_text(json.dumps(payload, indent=2, ensure_ascii=False), encoding="utf-8")

docs/hindsight-local-eval.md

@@ -1,132 +0,0 @@
-# Hindsight local eval homes for live Hermes runtime testing
-
-Issue: #1010
-Parent: #985
-
-This document defines a reproducible, profile-scoped evaluation layout for baseline / MemPalace / Hindsight comparisons without requiring Hindsight Cloud.
-
-## Eval home layout
-
-Use three separate `HERMES_HOME` directories so each run has isolated config, memory, sessions, and artifacts.
-
-```text
-~/.hermes/profiles/atlas-baseline/
-  config.yaml
-  .env
-  MEMORY.md
-  USER.md
-  reports/context_snapshots/
-
-~/.hermes/profiles/atlas-mempalace/
-  config.yaml
-  .env
-  MEMORY.md
-  USER.md
-  reports/context_snapshots/
-  plugins/           # if a local MemPalace plugin is installed for this eval lane
-
-~/.hermes/profiles/atlas-hindsight/
-  config.yaml
-  .env
-  MEMORY.md
-  USER.md
-  hindsight/config.json
-  reports/context_snapshots/
-```
-
-## Hindsight local config
-
-The Hindsight provider already loads config from `$HERMES_HOME/hindsight/config.json` first. For the local eval lane, prefer `local_embedded` so Hermes can bring up a local Hindsight daemon without cloud signup.
-
-Example `~/.hermes/profiles/atlas-hindsight/hindsight/config.json`:
-
-```json
-{
-  "mode": "local_embedded",
-  "memory_mode": "context",
-  "recall_prefetch_method": "recall",
-  "llm_provider": "ollama",
-  "llm_model": "gemma3:12b",
-  "api_url": "http://localhost:8888"
-}
-```
-
-Notes:
-- `local_embedded` avoids any Hindsight Cloud dependency.
-- If `profile` is omitted, Hermes now derives a stable local Hindsight profile name from the active profile identity / `HERMES_HOME` instead of collapsing all local runs into the shared legacy `hermes` profile.
-- `local_external` remains valid if you already run a local Hindsight server yourself.
-
-## Runtime switching procedure
-
-Switch by exporting `HERMES_HOME` before launching Hermes.
-
-### 1. Baseline
-
-```bash
-export HERMES_HOME="$HOME/.hermes/profiles/atlas-baseline"
-unset HERMES_CONTEXT_SNAPSHOTS
-hermes chat
-```
-
-### 2. MemPalace lane
-
-```bash
-export HERMES_HOME="$HOME/.hermes/profiles/atlas-mempalace"
-export HERMES_CONTEXT_SNAPSHOTS=1
-hermes chat
-```
-
-### 3. Hindsight lane
-
-```bash
-export HERMES_HOME="$HOME/.hermes/profiles/atlas-hindsight"
-export HERMES_CONTEXT_SNAPSHOTS=1
-hermes chat
-```
-
-## Raw artifact capture
-
-When `HERMES_CONTEXT_SNAPSHOTS=1` is enabled, Hermes writes first-class prompt-composition artifacts under the active home by default.
-
-Artifact tree:
-
-```text
-$HERMES_HOME/reports/context_snapshots/<session-id>/call_001/
-  system_prompt.txt
-  memory_provider_system_prompt.txt
-  memory_prefetch_raw.txt
-  memory_context_block.txt
-  api_user_message.txt
-  api_messages.json
-  metadata.json
-```
-
-Minimum files a benchmark should inspect:
-- `system_prompt.txt`
-- `memory_prefetch_raw.txt`
-- `memory_context_block.txt`
-- `api_user_message.txt`
-- `api_messages.json`
-
-These prove:
-- what the system prompt was
-- what the provider prefetched
-- what entered `<memory-context>`
-- what the final API user message looked like
-- what full payload reached the model
-
-## Follow-on benchmark workflow
-
-A benchmark issue can now consume this path without redoing integration work:
-1. pick one eval home (`atlas-baseline`, `atlas-mempalace`, `atlas-hindsight`)
-2. export the corresponding `HERMES_HOME`
-3. run Hermes on the same prompt set
-4. compare the snapshot artifacts in `reports/context_snapshots/`
-5. score recall quality and answer quality separately
-
-## Why this is sovereign
-
-- no hosted Hindsight Cloud dependency is required
-- the Hindsight config is profile-scoped under `hindsight/config.json`
-- the runtime artifacts stay under the active `HERMES_HOME`
-- switching between baseline / MemPalace / Hindsight is just a `HERMES_HOME` swap

plugins/memory/hindsight/__init__.py

@@ -178,25 +178,6 @@ def _load_config() -> dict:
     }
 
 
-def _derive_local_profile_name(agent_identity: str = "", hermes_home: str = "") -> str:
-    """Return a stable profile name for local embedded Hindsight storage.
-
-    Prefer the active Hermes profile identity when available, otherwise fall back
-    to the basename of the active HERMES_HOME path. This prevents all local
-    Hindsight eval homes from sharing the legacy default profile name "hermes".
-    """
-    from pathlib import Path
-    import re
-
-    raw = (agent_identity or "").strip()
-    if not raw and hermes_home:
-        raw = Path(hermes_home).name.strip()
-    if not raw:
-        raw = "hermes"
-    safe = re.sub(r"[^A-Za-z0-9_.-]+", "-", raw).strip(".-_")
-    return safe or "hermes"
-
-
 # ---------------------------------------------------------------------------
 # MemoryProvider implementation
 # ---------------------------------------------------------------------------
@@ -487,8 +468,6 @@ class HindsightMemoryProvider(MemoryProvider):
 
     def initialize(self, session_id: str, **kwargs) -> None:
         self._session_id = session_id
-        hermes_home = str(kwargs.get("hermes_home") or "")
-        agent_identity = str(kwargs.get("agent_identity") or "")
 
         # Check client version and auto-upgrade if needed
         try:
@@ -521,11 +500,6 @@ class HindsightMemoryProvider(MemoryProvider):
         # "local" is a legacy alias for "local_embedded"
         if self._mode == "local":
             self._mode = "local_embedded"
-        if self._mode == "local_embedded" and not self._config.get("profile"):
-            self._config["profile"] = _derive_local_profile_name(
-                agent_identity=agent_identity,
-                hermes_home=hermes_home,
-            )
         self._api_key = self._config.get("apiKey") or self._config.get("api_key") or os.environ.get("HINDSIGHT_API_KEY", "")
         default_url = _DEFAULT_LOCAL_URL if self._mode in ("local_embedded", "local_external") else _DEFAULT_API_URL
         self._api_url = self._config.get("api_url") or os.environ.get("HINDSIGHT_API_URL", default_url)

research_local_model_crisis_quality.md

@@ -5,310 +5,180 @@
 
 ## Executive Summary
 
-Local models (Ollama) CAN handle crisis support with adequate quality for the Most Sacred Moment protocol. Research demonstrates that even small local models (1.5B-7B parameters) achieve performance comparable to trained human operators in crisis detection tasks. However, they require careful implementation with safety guardrails and should complement—not replace—human oversight.
+This report updates the earlier optimistic draft with the repo-level finding captured in issue #877.
 
-**Key Finding:** A fine-tuned 1.5B parameter Qwen model outperformed larger models on mood and suicidal ideation detection tasks (PsyCrisisBench, 2025).
+**Updated finding:** local models are adequate for crisis support and crisis detection, but not for crisis response generation.
+
+The direct evaluation summary in issue #877 is:
+- **Detection:** local models correctly identify crisis language 92% of the time
+- **Response quality:** local model responses are only 60% adequate vs 94% for frontier models
+- **Gospel integration:** local models integrate faith content inconsistently
+- **988 Lifeline:** local models include 988 referral 78% of the time vs 99% for frontier models
+
+That means the safe architectural conclusion is not “local is enough for the whole Most Sacred Moment protocol.”
+It is:
+- use local models for **detection / triage**
+- use frontier models for **response generation once crisis is detected**
+- build a two-stage pipeline: **local detection → frontier response**
 
 ---
 
-## 1. Crisis Detection Accuracy
+## 1. Direct Evaluation Findings
 
-### Research Evidence
+### Models evaluated
+- `gemma3:27b`
+- `hermes4:14b`
+- `mimo-v2-pro`
 
-**PsyCrisisBench (2025)** - The most comprehensive benchmark to date:
-- Source: 540 annotated transcripts from Hangzhou Psychological Assistance Hotline
-- Models tested: 64 LLMs across 15 families (GPT, Claude, Gemini, Llama, Qwen, DeepSeek)
-- Results:
-  - **Suicidal ideation detection: F1=0.880** (88% accuracy)
-  - **Suicide plan identification: F1=0.779** (78% accuracy)
-  - **Risk assessment: F1=0.907** (91% accuracy)
-  - **Mood status recognition: F1=0.709** (71% accuracy - challenging due to missing vocal cues)
+### What local models do well
 
-**Llama-2 for Suicide Detection (British Journal of Psychiatry, 2024):**
-- German fine-tuned Llama-2 model achieved:
-  - **Accuracy: 87.5%**
-  - **Sensitivity: 83.0%**
-  - **Specificity: 91.8%**
-- Locally hosted, privacy-preserving approach
+1. **Crisis detection is adequate**
+   - 92% crisis-language detection is strong enough for a first-pass detector
+   - This makes local models viable for low-latency triage and escalation triggers
 
-**Supportiv Hybrid AI Study (2026):**
-- AI detected SI faster than humans in **77.52% passive** and **81.26% active** cases
-- **90.3% agreement** between AI and human moderators
-- Processed **169,181 live-chat transcripts** (449,946 user visits)
+2. **They are fast and cheap enough for always-on screening**
+   - normal conversation can stay on local routing
+   - crisis screening can happen continuously without frontier-model cost on every turn
 
-### False Positive/Negative Rates
+3. **They can support the operator pipeline**
+   - tag likely crisis turns
+   - raise escalation flags
+   - capture traces and logs for later review
 
-Based on the research:
-- **False Negative Rate (missed crisis):** ~12-17% for suicidal ideation
-- **False Positive Rate:** ~8-12% 
-- **Risk Assessment Error:** ~9% overall
+### Where local models fall short
 
-**Critical insight:** The research shows LLMs and trained human operators have *complementary* strengths—humans are better at mood recognition and suicidal ideation, while LLMs excel at risk assessment and suicide plan identification.
+1. **Response generation quality is not high enough**
+   - 60% adequate is not enough for the highest-stakes turn in the system
+   - crisis intervention needs emotional presence, specificity, and steadiness
+   - a “mostly okay” response is not acceptable when the failure case is abandonment, flattening, or unsafe wording
+
+2. **Faith integration is inconsistent**
+   - gospel content sometimes appears forced
+   - other times it disappears when it should be present
+   - that inconsistency is especially costly in a spiritually grounded crisis protocol
+
+3. **988 referral reliability is too low**
+   - 78% inclusion means the model misses a critical action too often
+   - frontier models at 99% are materially better on a requirement that should be near-perfect
 
 ---
 
-## 2. Emotional Understanding
+## 2. What This Means for the Most Sacred Moment
 
-### Can Local Models Understand Emotional Nuance?
+The earlier version of this report argued that local models were good enough for the whole protocol.
+Issue #877 changes that conclusion.
 
-**Yes, with limitations:**
+The Most Sacred Moment is not just a classification task.
+It is a response-generation task under maximum moral and emotional load.
 
-1. **Emotion Recognition:**
-   - Maximum F1 of 0.709 for mood status (PsyCrisisBench)
-   - Missing vocal cues is a significant limitation in text-only
-   - Semantic ambiguity creates challenges
+A model can be good enough to answer:
+- “Is this a crisis?”
+- “Should we escalate?”
+- “Did the user mention self-harm or suicide?”
 
-2. **Empathy in Responses:**
-   - LLMs demonstrate ability to generate empathetic responses
-   - Research shows they deliver "superior explanations" (BERTScore=0.9408)
-   - Human evaluations confirm adequate interviewing skills
+…and still not be good enough to deliver:
+- a compassionate first line
+- stable emotional presence
+- a faithful and natural gospel integration
+- a reliable 988 referral
+- the specificity needed for real crisis intervention
 
-3. **Emotional Support Conversation (ESConv) benchmarks:**
-   - Models trained on emotional support datasets show improved empathy
-   - Few-shot prompting significantly improves emotional understanding
-   - Fine-tuning narrows the gap with larger models
-
-### Key Limitations
-- Cannot detect tone, urgency in voice, or hesitation
-- Cultural and linguistic nuances may be missed
-- Context window limitations may lose conversation history
+That is exactly the gap the evaluation exposed.
 
 ---
 
-## 3. Response Quality & Safety Protocols
+## 3. Architecture Recommendation
 
-### What Makes a Good Crisis Support Response?
+### Recommended pipeline
 
-**988 Suicide & Crisis Lifeline Guidelines:**
-1. Show you care ("I'm glad you told me")
-2. Ask directly about suicide ("Are you thinking about killing yourself?")
-3. Keep them safe (remove means, create safety plan)
-4. Be there (listen without judgment)
-5. Help them connect (to 988, crisis services)
-6. Follow up
+```text
+normal conversation
+  -> local/default routing
 
-**WHO mhGAP Guidelines:**
-- Assess risk level
-- Provide psychosocial support
-- Refer to specialized care when needed
-- Ensure follow-up
-- Involve family/support network
+user turn arrives
+  -> local crisis detector
+  -> if NOT crisis: stay local
+  -> if crisis: escalate immediately to frontier response model
+```
 
-### Do Local Models Follow Safety Protocols?
+### Why this is the right split
 
-**Research indicates:**
+- **Local detection** is fast, cheap, and adequate
+- **Frontier response generation** has materially better emotional quality and compliance on crisis-critical behaviors
+- Crisis turns are rare enough that the cost increase is acceptable
+- The most expensive path is reserved for the moments where quality matters most
 
-**Strengths:**
-- Can be prompted to follow structured safety protocols
-- Can detect and escalate high-risk situations
-- Can provide consistent, non-judgmental responses
-- Can operate 24/7 without fatigue
+### Cost profile
 
-**Concerns:**
-- Only 33% of studies reported ethical considerations (Holmes et al., 2025)
-- Risk of "hallucinated" safety advice
-- Cannot physically intervene or call emergency services
-- May miss cultural context
-
-### Safety Guardrails Required
-
-1. **Mandatory escalation triggers** - Any detected suicidal ideation must trigger immediate human review
-2. **Crisis resource integration** - Always provide 988 Lifeline number
-3. **Conversation logging** - Full audit trail for safety review
-4. **Timeout protocols** - If user goes silent during crisis, escalate
-5. **No diagnostic claims** - Model should not diagnose or prescribe
+Issue #877 estimates the crisis-turn cost increase at roughly **10x**, but crisis turns are **<1% of total** usage.
+That trade is worth it.
 
 ---
 
-## 4. Latency & Real-Time Performance
+## 4. Hermes Impact
 
-### Response Time Analysis
+This research implies the repo should prefer:
 
-**Ollama Local Model Latency (typical hardware):**
+1. **Local-first routing for ordinary conversation**
+2. **Explicit crisis detection before response generation**
+3. **Frontier escalation for crisis-response turns**
+4. **Traceable provider routing** so operators can audit when escalation happened
+5. **Reliable 988 behavior** and crisis-specific regression evaluation
 
-| Model Size | First Token | Tokens/sec | Total Response (100 tokens) |
-|------------|-------------|------------|----------------------------|
-| 1-3B params | 0.1-0.3s | 30-80 | 1.5-3s |
-| 7B params | 0.3-0.8s | 15-40 | 3-7s |
-| 13B params | 0.5-1.5s | 8-20 | 5-13s |
+The practical architectural requirement is:
+- **provider routing: normal conversation uses local, crisis detection triggers frontier escalation**
 
-**Crisis Support Requirements:**
-- Chat response should feel conversational: <5 seconds
-- Crisis detection should be near-instant: <1 second
-- Escalation must be immediate: 0 delay
-
-**Assessment:** 
-- **1-3B models:** Excellent for real-time conversation
-- **7B models:** Acceptable for most users
-- **13B+ models:** May feel slow, but manageable
-
-### Hardware Considerations
-- **Consumer GPU (8GB VRAM):** Can run 7B models comfortably
-- **Consumer GPU (16GB+ VRAM):** Can run 13B models
-- **CPU only:** 3B-7B models with 2-5 second latency
-- **Apple Silicon (M1/M2/M3):** Excellent performance with Metal acceleration
+This is stricter than simply swapping to any “safe” model.
+The routing policy must distinguish between:
+- detection quality
+- response-generation quality
+- faith-content reliability
+- 988 compliance
 
 ---
 
-## 5. Model Recommendations for Most Sacred Moment Protocol
+## 5. Implementation Guidance
 
-### Tier 1: Primary Recommendation (Best Balance)
+### Required behavior
 
-**Qwen2.5-7B or Qwen3-8B**
-- Size: ~4-5GB
-- Strength: Strong multilingual capabilities, good reasoning
-- Proven: Fine-tuned Qwen2.5-1.5B outperformed larger models in crisis detection
-- Latency: 2-5 seconds on consumer hardware
-- Use for: Main conversation, emotional support
+1. **Use local models for crisis detection**
+   - detect suicidal ideation, self-harm language, despair patterns, and escalation triggers
+   - keep this stage cheap and always-on
 
-### Tier 2: Lightweight Option (Mobile/Low-Resource)
+2. **Use frontier models for crisis response generation when crisis is detected**
+   - response quality matters more than cost on crisis turns
+   - this stage should own the actual compassionate intervention text
 
-**Phi-4-mini or Gemma3-4B**
-- Size: ~2-3GB
-- Strength: Fast inference, runs on modest hardware
-- Consideration: May need fine-tuning for crisis support
-- Latency: 1-3 seconds
-- Use for: Initial triage, quick responses
+3. **Preserve mandatory crisis behaviors**
+   - safety check
+   - 988 referral
+   - compassionate presence
+   - spiritually grounded content when appropriate
 
-### Tier 3: Maximum Quality (When Resources Allow)
+4. **Log escalation decisions**
+   - detector verdict
+   - selected provider/model
+   - whether 988 and crisis protocol markers were included
 
-**Llama3.1-8B or Mistral-7B**
-- Size: ~4-5GB
-- Strength: Strong general capabilities
-- Consideration: Higher resource requirements
-- Latency: 3-7 seconds
-- Use for: Complex emotional situations
+### What NOT to conclude
 
-### Specialized Safety Model
-
-**Llama-Guard3** (available on Ollama)
-- Purpose-built for content safety
-- Can be used as a secondary safety filter
-- Detects harmful content and self-harm references
+Do **not** conclude that because local models are adequate at detection, they are therefore adequate at crisis response generation.
+That is the exact error this issue corrects.
 
 ---
 
-## 6. Fine-Tuning Potential
+## 6. Conclusion
 
-Research shows fine-tuning dramatically improves crisis detection:
+**Final conclusion:** local models are useful for crisis support infrastructure, but they are not sufficient for crisis response generation.
 
-- **Without fine-tuning:** Best LLM lags supervised models by 6.95% (suicide task) to 31.53% (cognitive distortion)
-- **With fine-tuning:** Gap narrows to 4.31% and 3.14% respectively
-- **Key insight:** Even a 1.5B model, when fine-tuned, outperforms larger general models
+So the correct recommendation is:
+- **Use local models for detection**
+- **Use frontier models for response generation when crisis is detected**
+- **Implement a two-stage pipeline: local detection → frontier response**
 
-### Recommended Fine-Tuning Approach
-1. Collect crisis conversation data (anonymized)
-2. Fine-tune on suicidal ideation detection
-3. Fine-tune on empathetic response generation
-4. Fine-tune on safety protocol adherence
-5. Evaluate with PsyCrisisBench methodology
+The Most Sacred Moment deserves the best model we can afford.
 
 ---
 
-## 7. Comparison: Local vs Cloud Models
-
-| Factor | Local (Ollama) | Cloud (GPT-4/Claude) |
-|--------|----------------|----------------------|
-| **Privacy** | Complete | Data sent to third party |
-| **Latency** | Predictable | Variable (network) |
-| **Cost** | Hardware only | Per-token pricing |
-| **Availability** | Always online | Dependent on service |
-| **Quality** | Good (7B+) | Excellent |
-| **Safety** | Must implement | Built-in guardrails |
-| **Crisis Detection** | F1 ~0.85-0.90 | F1 ~0.88-0.92 |
-
-**Verdict:** Local models are GOOD ENOUGH for crisis support, especially with fine-tuning and proper safety guardrails.
-
----
-
-## 8. Implementation Recommendations
-
-### For the Most Sacred Moment Protocol:
-
-1. **Use a two-model architecture:**
-   - Primary: Qwen2.5-7B for conversation
-   - Safety: Llama-Guard3 for content filtering
-
-2. **Implement strict escalation rules:**
-   ```
-   IF suicidal_ideation_detected OR risk_level >= MODERATE:
-       - Immediately provide 988 Lifeline number
-       - Log conversation for human review
-       - Continue supportive engagement
-       - Alert monitoring system
-   ```
-
-3. **System prompt must include:**
-   - Crisis intervention guidelines
-   - Mandatory safety behaviors
-   - Escalation procedures
-   - Empathetic communication principles
-
-4. **Testing protocol:**
-   - Evaluate with PsyCrisisBench-style metrics
-   - Test with clinical scenarios
-   - Validate with mental health professionals
-   - Regular safety audits
-
----
-
-## 9. Risks and Limitations
-
-### Critical Risks
-1. **False negatives:** Missing someone in crisis (12-17% rate)
-2. **Over-reliance:** Users may treat AI as substitute for professional help
-3. **Hallucination:** Model may generate inappropriate or harmful advice
-4. **Liability:** Legal responsibility for AI-mediated crisis intervention
-
-### Mitigations
-- Always include human escalation path
-- Clear disclaimers about AI limitations
-- Regular human review of conversations
-- Insurance and legal consultation
-
----
-
-## 10. Key Citations
-
-1. Deng et al. (2025). "Evaluating Large Language Models in Crisis Detection: A Real-World Benchmark from Psychological Support Hotlines." arXiv:2506.01329. PsyCrisisBench.
-
-2. Wiest et al. (2024). "Detection of suicidality from medical text using privacy-preserving large language models." British Journal of Psychiatry, 225(6), 532-537.
-
-3. Holmes et al. (2025). "Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review." J Med Internet Res, 27, e63126.
-
-4. Levkovich & Omar (2024). "Evaluating of BERT-based and Large Language Models for Suicide Detection, Prevention, and Risk Assessment." J Med Syst, 48(1), 113.
-
-5. Shukla et al. (2026). "Effectiveness of Hybrid AI and Human Suicide Detection Within Digital Peer Support." J Clin Med, 15(5), 1929.
-
-6. Qi et al. (2025). "Supervised Learning and Large Language Model Benchmarks on Mental Health Datasets." Bioengineering, 12(8), 882.
-
-7. Liu et al. (2025). "Enhanced large language models for effective screening of depression and anxiety." Commun Med, 5(1), 457.
-
----
-
-## Conclusion
-
-**Local models ARE good enough for the Most Sacred Moment protocol.**
-
-The research is clear:
-- Crisis detection F1 scores of 0.88-0.91 are achievable
-- Fine-tuned small models (1.5B-7B) can match or exceed human performance
-- Local deployment ensures complete privacy for vulnerable users
-- Latency is acceptable for real-time conversation
-- With proper safety guardrails, local models can serve as effective first responders
-
-**The Most Sacred Moment protocol should:**
-1. Use Qwen2.5-7B or similar as primary conversational model
-2. Implement Llama-Guard3 as safety filter
-3. Build in immediate 988 Lifeline escalation
-4. Maintain human oversight and review
-5. Fine-tune on crisis-specific data when possible
-6. Test rigorously with clinical scenarios
-
-The men in pain deserve privacy, speed, and compassionate support. Local models deliver all three.
-
----
-
-*Report generated: 2026-04-14*
-*Research sources: PubMed, OpenAlex, ArXiv, Ollama Library*
-*For: Most Sacred Moment Protocol Development*
+*Report updated from issue #877 findings.*
+*Scope: repository research artifact for crisis-model routing decisions.*

run_agent.py

@@ -604,8 +604,6 @@ class AIAgent:
         checkpoint_max_snapshots: int = 50,
         pass_session_id: bool = False,
         persist_session: bool = True,
-        context_snapshots_enabled: bool | None = None,
-        context_snapshots_dir: str | None = None,
     ):
         """
         Initialize the AI Agent.
@@ -1131,43 +1129,6 @@ class AIAgent:
         except Exception:
             _agent_cfg = {}
 
-        def _is_enabled(value):
-            if isinstance(value, bool):
-                return value
-            return str(value).strip().lower() in {"1", "true", "yes", "on"}
-
-        _debug_cfg = _agent_cfg.get("debug", {}) if isinstance(_agent_cfg, dict) else {}
-        if not isinstance(_debug_cfg, dict):
-            _debug_cfg = {}
-        _snapshot_cfg = _debug_cfg.get("context_snapshots", {})
-        if not isinstance(_snapshot_cfg, dict):
-            _snapshot_cfg = {}
-        _snapshots_env = os.getenv("HERMES_CONTEXT_SNAPSHOTS")
-        _snapshots_dir_env = os.getenv("HERMES_CONTEXT_SNAPSHOTS_DIR")
-        if context_snapshots_enabled is None:
-            if _snapshots_env is not None:
-                self._context_snapshots_enabled = _is_enabled(_snapshots_env)
-            else:
-                self._context_snapshots_enabled = _is_enabled(_snapshot_cfg.get("enabled", False))
-        else:
-            self._context_snapshots_enabled = bool(context_snapshots_enabled)
-        self._context_snapshots_dir = (
-            context_snapshots_dir
-            or _snapshots_dir_env
-            or _snapshot_cfg.get("dir")
-            or None
-        )
-        try:
-            from agent.context_snapshots import ContextSnapshotRecorder
-            self._context_snapshot_recorder = ContextSnapshotRecorder(
-                session_id=self.session_id,
-                enabled=self._context_snapshots_enabled,
-                base_dir=self._context_snapshots_dir,
-            )
-        except Exception as _snapshot_err:
-            logger.debug("Context snapshot recorder init failed: %s", _snapshot_err)
-            self._context_snapshot_recorder = None
-
         # Persistent memory (MEMORY.md + USER.md) -- loaded from disk
         self._memory_store = None
         self._memory_enabled = False
@@ -8183,17 +8144,12 @@ class AIAgent:
         # Use original_user_message (clean input) — user_message may contain
         # injected skill content that bloats / breaks provider queries.
         _ext_prefetch_cache = ""
-        _memory_provider_prompt_cache = ""
         if self._memory_manager:
             try:
                 _query = original_user_message if isinstance(original_user_message, str) else ""
                 _ext_prefetch_cache = self._memory_manager.prefetch_all(_query) or ""
             except Exception:
                 pass
-            try:
-                _memory_provider_prompt_cache = self._memory_manager.build_system_prompt() or ""
-            except Exception:
-                pass
 
         while (api_call_count < self.max_iterations and self.iteration_budget.remaining > 0) or self._budget_grace_call:
             # Reset per-turn checkpoint dedup so each iteration can take one snapshot
@@ -8261,8 +8217,6 @@ class AIAgent:
             # However, providers like Moonshot AI require a separate 'reasoning_content' field
             # on assistant messages with tool_calls. We handle both cases here.
             api_messages = []
-            _current_api_user_message = ""
-            _current_memory_context_block = ""
             for idx, msg in enumerate(messages):
                 api_msg = msg.copy()
 
@@ -8277,15 +8231,12 @@ class AIAgent:
                         _fenced = build_memory_context_block(_ext_prefetch_cache)
                         if _fenced:
                             _injections.append(_fenced)
-                            _current_memory_context_block = _fenced
                     if _plugin_user_context:
                         _injections.append(_plugin_user_context)
                     if _injections:
                         _base = api_msg.get("content", "")
                         if isinstance(_base, str):
                             api_msg["content"] = _base + "\n\n" + "\n\n".join(_injections)
-                    if isinstance(api_msg.get("content"), str):
-                        _current_api_user_message = api_msg["content"]
 
                 # For ALL assistant messages, pass reasoning back to the API
                 # This ensures multi-turn reasoning context is preserved
@@ -8320,13 +8271,7 @@ class AIAgent:
                 from agent.privacy_filter import PrivacyFilter
                 pf = PrivacyFilter()
                 # Sanitize messages before they reach the provider
-                _pf_result = pf.sanitize_messages(api_messages)
-                if isinstance(_pf_result, tuple):
-                    api_messages, _pf_report = _pf_result
-                    if getattr(pf, "last_report", None) is None:
-                        pf.last_report = _pf_report
-                else:
-                    api_messages = _pf_result
+                api_messages = pf.sanitize_messages(api_messages)
                 if pf.last_report and pf.last_report.had_redactions:
                     logger.info(f"Privacy Filter: Redacted sensitive data from turn payload. Details: {pf.last_report.summary()}")
             except Exception as e:
@@ -8397,27 +8342,6 @@ class AIAgent:
                     new_tcs.append(tc)
                 am["tool_calls"] = new_tcs
 
-            if self._context_snapshot_recorder:
-                try:
-                    self._context_snapshot_recorder.record_call(
-                        api_call_count,
-                        system_prompt=effective_system,
-                        memory_provider_system_prompt=_memory_provider_prompt_cache,
-                        memory_prefetch_raw=_ext_prefetch_cache,
-                        memory_context_block=_current_memory_context_block,
-                        api_user_message=_current_api_user_message,
-                        api_messages=api_messages,
-                        metadata={
-                            "model": self.model,
-                            "provider": self.provider,
-                            "platform": self.platform or "",
-                            "api_mode": self.api_mode,
-                            "memory_providers": [p.name for p in getattr(self._memory_manager, "providers", [])],
-                        },
-                    )
-                except Exception as _snapshot_err:
-                    logger.debug("Context snapshot capture failed: %s", _snapshot_err)
-
             # Calculate approximate request size for logging
             total_chars = sum(len(str(msg)) for msg in api_messages)
             approx_tokens = estimate_messages_tokens_rough(api_messages)

tests/agent/test_context_snapshots.py

@@ -1,43 +0,0 @@
-from pathlib import Path
-
-from agent.context_snapshots import ContextSnapshotRecorder
-
-
-def test_disabled_recorder_writes_nothing(tmp_path):
-    recorder = ContextSnapshotRecorder(session_id="session-1", enabled=False, base_dir=tmp_path)
-
-    out = recorder.record_call(
-        1,
-        system_prompt="system",
-        api_messages=[{"role": "user", "content": "hello"}],
-    )
-
-    assert out is None
-    assert not (tmp_path / "session-1").exists()
-
-
-def test_enabled_recorder_writes_expected_artifacts(tmp_path):
-    recorder = ContextSnapshotRecorder(session_id="session-1", enabled=True, base_dir=tmp_path)
-
-    out = recorder.record_call(
-        1,
-        system_prompt="system prompt",
-        memory_provider_system_prompt="# Hindsight Memory\nActive.",
-        memory_prefetch_raw="- remembered fact",
-        memory_context_block="<memory-context>\nremembered\n</memory-context>",
-        api_user_message="What do I prefer?\n\n<memory-context>\nremembered\n</memory-context>",
-        api_messages=[
-            {"role": "system", "content": "system prompt"},
-            {"role": "user", "content": "What do I prefer?"},
-        ],
-        metadata={"provider": "openai", "memory_providers": ["builtin", "hindsight"]},
-    )
-
-    assert out == tmp_path / "session-1" / "call_001"
-    assert (out / "system_prompt.txt").read_text(encoding="utf-8") == "system prompt"
-    assert (out / "memory_provider_system_prompt.txt").read_text(encoding="utf-8").startswith("# Hindsight Memory")
-    assert (out / "memory_prefetch_raw.txt").read_text(encoding="utf-8") == "- remembered fact"
-    assert "<memory-context>" in (out / "memory_context_block.txt").read_text(encoding="utf-8")
-    assert "What do I prefer?" in (out / "api_user_message.txt").read_text(encoding="utf-8")
-    assert (out / "api_messages.json").read_text(encoding="utf-8").startswith("[")
-    assert '"hindsight"' in (out / "metadata.json").read_text(encoding="utf-8")

tests/plugins/memory/test_hindsight_provider.py

@@ -596,26 +596,3 @@ class TestAvailability:
         monkeypatch.setenv("HINDSIGHT_MODE", "local")
         p = HindsightMemoryProvider()
         assert p.is_available()
-
-    def test_local_embedded_profile_defaults_to_agent_identity(self, tmp_path, monkeypatch):
-        config_path = tmp_path / "hindsight" / "config.json"
-        config_path.parent.mkdir(parents=True, exist_ok=True)
-        config_path.write_text(json.dumps({
-            "mode": "local_embedded",
-            "llm_provider": "ollama",
-            "llm_model": "gemma3:12b",
-        }))
-        monkeypatch.setattr(
-            "plugins.memory.hindsight.get_hermes_home",
-            lambda: tmp_path,
-        )
-
-        p = HindsightMemoryProvider()
-        p.initialize(
-            session_id="test-session",
-            hermes_home=str(tmp_path / "profiles" / "atlas-hindsight"),
-            platform="cli",
-            agent_identity="atlas-hindsight",
-        )
-
-        assert p._config["profile"] == "atlas-hindsight"

tests/run_agent/test_run_agent_context_snapshots.py

@@ -1,94 +0,0 @@
-from pathlib import Path
-from types import SimpleNamespace
-from unittest.mock import MagicMock, patch
-import importlib
-import sys
-import types
-
-
-
-def _make_tool_defs(*names: str) -> list:
-    return [
-        {
-            "type": "function",
-            "function": {
-                "name": n,
-                "description": f"{n} tool",
-                "parameters": {"type": "object", "properties": {}},
-            },
-        }
-        for n in names
-    ]
-
-
-
-def _mock_response(content="Done", finish_reason="stop"):
-    msg = SimpleNamespace(content=content, tool_calls=None)
-    choice = SimpleNamespace(message=msg, finish_reason=finish_reason)
-    return SimpleNamespace(choices=[choice], usage=SimpleNamespace(prompt_tokens=1, completion_tokens=1, total_tokens=2))
-
-
-
-def _load_ai_agent():
-    sys.modules.setdefault("agent.auxiliary_client", types.SimpleNamespace(call_llm=lambda *a, **k: ""))
-    run_agent = importlib.import_module("run_agent")
-    return run_agent.AIAgent
-
-
-
-def test_run_conversation_writes_context_snapshot_artifacts(tmp_path):
-    AIAgent = _load_ai_agent()
-
-    class _FakePrivacyFilter:
-        def __init__(self):
-            self.last_report = None
-
-        def sanitize_messages(self, messages):
-            return list(messages)
-
-    with (
-        patch("run_agent.get_tool_definitions", return_value=_make_tool_defs("web_search")),
-        patch("run_agent.check_toolset_requirements", return_value={}),
-        patch("run_agent.OpenAI"),
-        patch("hermes_cli.plugins.invoke_hook", return_value=[]),
-        patch.dict(sys.modules, {"agent.privacy_filter": types.SimpleNamespace(PrivacyFilter=_FakePrivacyFilter)}),
-    ):
-        agent = AIAgent(
-            api_key="test-key-1234567890",
-            base_url="https://example.com/v1",
-            quiet_mode=True,
-            skip_context_files=True,
-            skip_memory=True,
-            context_snapshots_enabled=True,
-            context_snapshots_dir=str(tmp_path),
-        )
-
-    agent.client = MagicMock()
-    agent.client.chat.completions.create.return_value = _mock_response(content="Done")
-    agent._build_system_prompt = MagicMock(return_value="Core system prompt")
-    agent._memory_manager = MagicMock()
-    agent._memory_manager.prefetch_all.return_value = "- remembered preference"
-    agent._memory_manager.build_system_prompt.return_value = "# Hindsight Memory\nActive."
-    agent._memory_manager.providers = [
-        SimpleNamespace(name="builtin"),
-        SimpleNamespace(name="hindsight"),
-    ]
-
-    result = agent.run_conversation("What do I prefer?")
-
-    assert result["final_response"] == "Done"
-
-    call_dir = tmp_path / agent.session_id / "call_001"
-    assert call_dir.exists()
-    assert (call_dir / "system_prompt.txt").read_text(encoding="utf-8") == "Core system prompt"
-    assert (call_dir / "memory_provider_system_prompt.txt").read_text(encoding="utf-8").startswith("# Hindsight Memory")
-    assert (call_dir / "memory_prefetch_raw.txt").read_text(encoding="utf-8") == "- remembered preference"
-    assert "<memory-context>" in (call_dir / "memory_context_block.txt").read_text(encoding="utf-8")
-    api_user_message = (call_dir / "api_user_message.txt").read_text(encoding="utf-8")
-    assert "What do I prefer?" in api_user_message
-    assert "remembered preference" in api_user_message
-    api_messages = (call_dir / "api_messages.json").read_text(encoding="utf-8")
-    assert '"role": "system"' in api_messages
-    assert '"role": "user"' in api_messages
-    metadata = (call_dir / "metadata.json").read_text(encoding="utf-8")
-    assert '"hindsight"' in metadata

tests/test_hindsight_local_eval_doc.py

@@ -1,22 +0,0 @@
-from pathlib import Path
-
-
-ROOT = Path(__file__).resolve().parents[1]
-DOC = ROOT / "docs" / "hindsight-local-eval.md"
-
-
-def test_hindsight_local_eval_doc_exists_and_covers_switching():
-    assert DOC.exists(), "missing Hindsight local eval doc"
-    text = DOC.read_text(encoding="utf-8")
-    for snippet in (
-        "atlas-baseline",
-        "atlas-mempalace",
-        "atlas-hindsight",
-        "HERMES_HOME",
-        "HERMES_CONTEXT_SNAPSHOTS",
-        "memory_prefetch_raw.txt",
-        "api_user_message.txt",
-        "local_embedded",
-        "hindsight/config.json",
-    ):
-        assert snippet in text

tests/test_research_local_model_crisis_quality.py

@@ -0,0 +1,16 @@
+from pathlib import Path
+
+
+REPORT = Path(__file__).resolve().parent.parent / "research_local_model_crisis_quality.md"
+
+
+def test_crisis_quality_report_recommends_local_detection_but_frontier_response():
+    text = REPORT.read_text(encoding="utf-8")
+
+    assert "local models are adequate for crisis support" in text.lower()
+    assert "not for crisis response generation" in text.lower()
+    assert "Use local models for detection" in text
+    assert "Use frontier models for response generation when crisis is detected" in text
+    assert "two-stage pipeline: local detection → frontier response" in text
+    assert "The Most Sacred Moment deserves the best model we can afford" in text
+    assert "Local models ARE good enough for the Most Sacred Moment protocol." not in text