Module: PWN::AI::Agent::Reward

Defined in:
lib/pwn/ai/agent/reward.rb

Overview

PWN::AI::Agent::Reward is the OUTCOME reward model for the pwn-ai reinforcement-learning loop. It replaces the regex-proxy reward that previously drove Learning.infer_success / Loop.record_metrics with four calibrated signals:

R1  .judge      — LLM Outcome Reward Model (ORM). Scores the FINAL
                answer against the user request → {score:0..1,
                verdict: :solved|:partial|:wrong|:refused,
                rationale:}. Scalar, not boolean.
R2  .prm        — Process Reward Model. Per-tool-call "did this
                step advance toward the goal?" → step_reward
                tagged onto every Sessions entry so credit is
                assignable INSIDE a trajectory, not just at its
                boundary. First PRM applied to security tooling.
R3  .sentinel   — Reward-hacking detector. Tracks proxy vs judge
                vs (1 - user_correction_rate); when they diverge
                by > SENTINEL_GAP the reward signal itself is
                fingerprinted as a Mistake so the operator sees
                "your success_rate is a lie" in KNOWN MISTAKES.
R4  .semantic_ok — Structured tool-result classifier. Knows that
                `grep exit 1` == "no match", not "failure";
                kills the phantom-mistake class (31f1871b8a15)
                that made the loop's #1 negative signal a false
                positive it created itself.

Reward also owns the PREFERENCE-PAIR ledger (~/.pwn/preferences.jsonl) that turns pwn's naturally-generated (rejected, chosen) pairs — from user corrections, mistakes_resolve, and Curriculum.counterfactual A/B branches — into a DPO export (W1). This is the ONLY path from in-context learning to weight-level policy improvement.

E3 .verify_as_reward — grounds any final containing a checkable claim (CVE / version / cited URL) via Extrospection.verify and maps the browser verdict onto the reward scalar. Hallucination becomes a measurable −reward, not just a warning.

Everything degrades gracefully: when module_reflection is off (no LLM judge available) .judge falls back to a calibrated heuristic over .semantic_ok + .verify_as_reward + Mistakes correction rate, which is STILL strictly better than the old regex.

Constant Summary collapse

PREFERENCES_FILE = 
File.join(Dir.home, '.pwn', 'preferences.jsonl')
SENTINEL_FILE = 
File.join(Dir.home, '.pwn', 'reward_sentinel.json')
DPO_DIR = 
File.join(Dir.home, '.pwn', 'finetune')
SENTINEL_GAP = 
0.15
SENTINEL_WINDOW = 
40
VERDICTS = 
{
  solved: 1.0, confirmed: 1.0, partial: 0.5,
  unknown: 0.5, wrong: 0.0, refused: 0.0, refuted: 0.0
}.freeze
BENIGN_EXIT =

Commands whose non-zero exit is INFORMATIONAL, not a failure. The regex-proxy treating these as failures was the single largest source of noise in Mistakes/Metrics (grep exit 1 = "no match").

{
  /\b(?:e|f|z|rip|p)?grep\b/ => [1],
  /\bdiff\b/ => [1],
  /\bcmp\b/ => [1],
  /\btest\b|\[\s/ => [1],
  /\bls\b/ => [1, 2],
  /\bfind\b/ => [1],
  /\bwhich\b|\bcommand -v\b/ => [1],
  /\bpidof\b|\bpgrep\b|\bpkill\b/ => [1],
  /\bxargs\b/ => [123],
  /\btimeout\b/ => [124],
  /\bcurl\b/ => [22],
  /\brubocop\b/ => [1]
}.freeze
JUDGE_SYSTEM = 
<<~SYS
  You are the pwn-ai Outcome Reward Model. Given a USER REQUEST, the
  agent's FINAL ANSWER, and a compressed TOOL TRACE, emit ONE line of
  strict JSON:
    {"score": <0.0-1.0>, "verdict": "solved|partial|wrong|refused",
     "rationale": "<≤140 chars>", "key_step": <int|-1>}
  score=1.0 only when the final DEMONSTRABLY satisfies the request
  (evidence in trace). score=0.5 for correct-direction-but-incomplete.
  score=0.0 for hallucinated / off-goal / refused. key_step is the
  1-indexed trace line most responsible for the outcome (credit
  assignment), or -1 if none. Output JSON ONLY.
SYS
PRM_SYSTEM = 
<<~SYS
  You are the pwn-ai Process Reward Model. For EACH numbered tool
  step, output one integer per line: 1 (advanced toward the goal),
  0 (neutral / exploratory), -1 (regressed / wasted). Output ONLY
  the integers, one per line, same count as steps. No prose.
SYS
DPO_SOURCE_CAP =

Max share any single preference source may occupy in a DPO export. Without this cap, mistakes_resolve monoculture (often >80%) teaches the LoRA "emit fix prose" instead of trajectory preference (P5 enforce).

0.40

Class Method Summary collapse

Class Method Details

.authors ⇒ Object

Author(s)

0day Inc. support@0dayinc.com



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# File 'lib/pwn/ai/agent/reward.rb', line 861

public_class_method def self.authors
  "AUTHOR(S):\n  0day Inc. <support@0dayinc.com>\n"
end

.clear_proxy_distrust ⇒ Object 



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# File 'lib/pwn/ai/agent/reward.rb', line 281

public_class_method def self.clear_proxy_distrust
  s = load_sentinel
  return if s[:proxy_distrust].to_f <= 0.0

  s[:proxy_distrust] = 0.0
  s[:distrust_cleared_at] = Time.now.utc.iso8601
  atomic_write(path: SENTINEL_FILE, body: JSON.generate(s))
rescue StandardError
  nil
end

.export_dpo(opts = {}) ⇒ Object 



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# File 'lib/pwn/ai/agent/reward.rb', line 461

public_class_method def self.export_dpo(opts = {})
  fmt = (opts[:format] || :dpo).to_sym
  FileUtils.mkdir_p(DPO_DIR)
  out = opts[:out] || File.join(DPO_DIR, "pwn-dpo-#{Time.now.utc.strftime('%Y%m%d')}.jsonl")
  rows = preferences(limit: 100_000)
  # P5 — downsample so no single source exceeds DPO_SOURCE_CAP of the export.
  # opt-out with balance: false (raw dump for diagnostics).
  balance = opts.key?(:balance) ? opts[:balance] : true
  selected = balance ? balance_preference_rows(rows: rows, cap: (opts[:source_cap] || DPO_SOURCE_CAP).to_f) : rows
  dropped = rows.length - selected.length
  File.open(out, 'w') do |f|
    selected.each do |r|
      line = case fmt
             when :kto
               [{ prompt: r[:prompt], completion: r[:chosen], label: true },
                { prompt: r[:prompt], completion: r[:rejected], label: false }]
             else
               # Keep source for auditability / preference_balance post-export.
               { prompt: r[:prompt], chosen: r[:chosen], rejected: r[:rejected], source: r[:source] }
             end
      (line.is_a?(Array) ? line : [line]).each { |l| f.puts(JSON.generate(l)) }
    end
  end
  by_src = selected.group_by { |r| r[:source].to_s }.transform_values(&:length)
  {
    path: out, format: fmt, pairs: selected.length, bytes: File.size(out),
    balanced: balance, dropped: dropped, by_source: by_src,
    source_cap: balance ? (opts[:source_cap] || DPO_SOURCE_CAP).to_f : nil
  }
end

.help ⇒ Object

Display Usage for this Module



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# File 'lib/pwn/ai/agent/reward.rb', line 867

public_class_method def self.help
  puts <<~USAGE
    USAGE:
      # Tier 1 — reward signal
      PWN::AI::Agent::Reward.judge(request: req, final: text, session_id: sid)     # R1 ORM → {score:, verdict:, rationale:}
      PWN::AI::Agent::Reward.prm(request: req, session_id: sid)                    # R2 PRM → per-step credit
      PWN::AI::Agent::Reward.sentinel                                              # R3 reward-hacking detector
      PWN::AI::Agent::Reward.reset_sentinel                                        # wipe corrupt window + distrust
      PWN::AI::Agent::Reward.semantic_ok(name: 'shell', raw: json, args: args)     # R4 kills phantom exit≠0 mistakes

      # Tier 5 — preference pairs → DPO
      PWN::AI::Agent::Reward.record_preference(prompt: p, rejected: r, chosen: c, source: :user_correction)
      PWN::AI::Agent::Reward.preferences(limit: 100)
      PWN::AI::Agent::Reward.export_dpo(format: :dpo)                              # W1 → ~/.pwn/finetune/pwn-dpo-*.jsonl (≤40%/source)
      PWN::AI::Agent::Reward.export_dpo(format: :dpo, balance: false)              # raw dump (diagnostics)

      # Tier 6 — grounded reward
      PWN::AI::Agent::Reward.verify_as_reward(final: text)                         # E3 browser-verified reward

      Config (PWN::Env[:ai][:agent]):
        :verify_as_reward   - Boolean/nil, ground finals via extro_verify (nil=auto)
        :reward_llm         - Boolean/nil, force ORM/PRM LLM teacher (nil=on for remote engines)

      #{self}.authors
  USAGE
end

.judge(opts = {}) ⇒ Object

Supported Method Parameters

v = PWN::AI::Agent::Reward.judge( request: 'required - original user request', final: 'required - assistant final answer', session_id: 'optional - PWN::Sessions id (adds tool trace)', trace: 'optional - Array of tool-result strings (overrides session_id)', commit: 'optional - write score into learning.jsonl / sentinel (default true)' )



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# File 'lib/pwn/ai/agent/reward.rb', line 114

public_class_method def self.judge(opts = {})
  request = opts[:request].to_s
  final   = opts[:final].to_s
  trace   = Array(opts[:trace])
  trace   = load_trace(session_id: opts[:session_id]) if trace.empty? && opts[:session_id]
  commit  = opts.key?(:commit) ? opts[:commit] : true

  v = llm_judge(request: request, final: final, trace: trace)
  v ||= heuristic_judge(request: request, final: final, trace: trace)

  ground = verify_as_reward(final: final)
  unless ground.nil?
    # Ground-truth override: a browser-refuted claim caps score at
    # 0.2 regardless of how confident the judge was; a confirmed
    # claim floors it at 0.6. E3.
    v[:score] = [v[:score], 0.2].min if ground[:verdict] == :refuted
    v[:score] = [v[:score], 0.6].max if ground[:verdict] == :confirmed
    v[:grounded] = ground
  end

  v[:success] = v[:score] >= 0.6
  record_sentinel(proxy: opts[:proxy_ok], judge: v[:score]) if commit
  v
rescue StandardError => e
  { score: 0.5, verdict: :unknown, rationale: "judge error: #{e.class}", success: !final.strip.empty?, error: e.message }
end

.preferences(opts = {}) ⇒ Object

Supported Method Parameters

rows = PWN::AI::Agent::Reward.preferences(limit: 500, source: nil)



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# File 'lib/pwn/ai/agent/reward.rb', line 435

public_class_method def self.preferences(opts = {})
  limit  = opts[:limit] || 500
  source = opts[:source].to_s
  return [] unless File.exist?(PREFERENCES_FILE)

  rows = File.readlines(PREFERENCES_FILE).map do |l|
    JSON.parse(l, symbolize_names: true)
  rescue StandardError
    nil
  end
  rows.compact!
  rows.select! { |r| r[:source] == source } unless source.empty?
  rows.reverse.first(limit)
end

.prm(opts = {}) ⇒ Object

Supported Method Parameters

steps = PWN::AI::Agent::Reward.prm( request: 'required - user goal', session_id: 'optional - session to score in place', trace: 'optional - Array of args:, result: or Strings' )

Returns [step:, reward: -1|0|1, ...] and, when session_id is given, rewrites each tool line in the transcript with a [step_reward=N] prefix so exemplars_for / distill_skill can keep only reward>0 steps (C4 minimal sufficient trace).



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# File 'lib/pwn/ai/agent/reward.rb', line 157

public_class_method def self.prm(opts = {})
  request = opts[:request].to_s
  trace   = Array(opts[:trace])
  sid     = opts[:session_id]
  trace   = load_trace(session_id: sid) if trace.empty? && sid

  rewards = llm_prm(request: request, trace: trace)
  rewards ||= heuristic_prm(trace: trace)

  out = trace.each_with_index.map do |s, i|
    { idx: i + 1, step: s.to_s[0, 200], reward: rewards[i] || 0 }
  end
  annotate_session(session_id: sid, rewards: rewards) if sid
  out
rescue StandardError
  []
end

.proxy_distrust ⇒ Object

P4 — scalar 0.0..1.0 haircut applied to Metrics success / Registry β when the proxy is lying. 0.0 = trust proxy fully; 1.0 = ignore proxy rates.



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# File 'lib/pwn/ai/agent/reward.rb', line 246

public_class_method def self.proxy_distrust
  s = load_sentinel
  d = s[:proxy_distrust].to_f
  # auto-expire after 7d without refresh so a one-off gap doesn't stick
  if s[:distrust_at]
    age = Time.now.utc - Time.parse(s[:distrust_at].to_s)
    return 0.0 if age > 7 * 86_400
  end
  d.clamp(0.0, 1.0)
rescue StandardError
  0.0
end

.record_preference(opts = {}) ⇒ Object

Supported Method Parameters

PWN::AI::Agent::Reward.record_preference( prompt: 'required - the context / user request', rejected: 'required - the losing completion / action', chosen: 'required - the winning completion / action', source: 'optional - :user_correction | :mistakes_resolve | :counterfactual | :critic' )



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# File 'lib/pwn/ai/agent/reward.rb', line 411

public_class_method def self.record_preference(opts = {})
  prompt   = opts[:prompt].to_s
  rejected = opts[:rejected].to_s
  chosen   = opts[:chosen].to_s
  return nil if prompt.strip.empty? || chosen.strip.empty? || rejected.strip.empty?
  return nil if chosen.strip == rejected.strip

  entry = {
    id: Digest::SHA256.hexdigest("#{prompt}|#{rejected}|#{chosen}")[0, 12],
    prompt: prompt[0, 4_000],
    rejected: rejected[0, 4_000],
    chosen: chosen[0, 4_000],
    source: (opts[:source] || :unknown).to_s,
    engine: (PWN::Env.dig(:ai, :active) if defined?(PWN::Env)).to_s,
    timestamp: Time.now.utc.iso8601
  }
  FileUtils.mkdir_p(File.dirname(PREFERENCES_FILE))
  File.open(PREFERENCES_FILE, 'a') { |f| f.puts(JSON.generate(entry)) }
  entry
end

.recoverable_shape(opts = {}) ⇒ Object

2.2 — coarse recoverable shape beside the fingerprint. Paths are normalised away for counting; shape stays for repair routing (enoent → install/check path; exit127 → missing binary; …).



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# File 'lib/pwn/ai/agent/reward.rb', line 349

public_class_method def self.recoverable_shape(opts = {})
  err = "#{opts[:err]} #{opts[:stderr]}".downcase
  ec  = opts[:exit_code]
  return :exit127 if ec == 127 || err.include?('command not found')
  return :exit126 if ec == 126
  return :enoent if err.match?(/no such file|enoent|cannot access|not a directory/)
  return :eacces if err.match?(/permission denied|eacces|operation not permitted/)
  return :auth_required if err.match?(/auth|unauthorized|401|403|forbidden|login required|api.?key/)
  return :timeout if ec == 124 || err.include?('timed out') || err.include?('timeout')
  return :network if err.match?(/connection refused|name or service not known|could not resolve|network is unreachable/)
  return :syntax if err.match?(/syntax error|parse error|unexpected token|json::parser/)
  return :nonzero_exit if ec && ec != 0
  return :handler_error if err.strip.length.positive?

  :unknown
end

.reset ⇒ Object 



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# File 'lib/pwn/ai/agent/reward.rb', line 492

public_class_method def self.reset
  FileUtils.rm_f(PREFERENCES_FILE)
  FileUtils.rm_f(SENTINEL_FILE)
  { cleared: true }
end

.reset_sentinel ⇒ Object

One-shot: wipe sentinel window + distrust after deploying the ring-buffer arithmetic (or any time the live file is known-corrupt). Does NOT touch preferences / DPO exports (unlike .reset).



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# File 'lib/pwn/ai/agent/reward.rb', line 295

public_class_method def self.reset_sentinel
  FileUtils.rm_f(SENTINEL_FILE)
  { cleared: true, path: SENTINEL_FILE }
end

.semantic_ok(opts = {}) ⇒ Object

Supported Method Parameters

h = PWN::AI::Agent::Reward.semantic_ok( name: 'required - tool name', raw: 'required - JSON string returned by Dispatch.call', args: 'optional - the tool call arguments (used for BENIGN_EXIT)' )

Returns { ok:, semantic_ok:, exit:, err:, benign: }. :ok is the old proxy (handler didn't raise); :semantic_ok additionally knows that grep/diff/find exit≠0 with empty stderr is not a failure. Loop.run records Metrics on :ok but only records Mistakes on !semantic_ok.



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# File 'lib/pwn/ai/agent/reward.rb', line 316

public_class_method def self.semantic_ok(opts = {})
  name = opts[:name].to_s
  raw  = opts[:raw].to_s
  ok   = raw.include?('"success":true')
  err  = raw[/"error":"([^"]{1,300})"/, 1]
  exit_code = raw[/"exit":(\d+)/, 1]&.to_i
  stderr    = raw[/"stderr":"([^"]{0,400})"/, 1].to_s

  benign = false
  shape  = nil
  if name == 'shell' && ok && exit_code && exit_code != 0
    cmd = extract_cmd(args: opts[:args])
    # 2.1 — ONLY BENIGN_EXIT regex × allowed codes. The old global
    # `stderr.empty? && exit==1 ⇒ benign` laundered real failures
    # (pipelines without pipefail, bare false, etc.) into "success".
    # For pipelines, match the LAST stage (post-pipe) first, then any.
    stages = cmd.split('|').map(&:strip)
    last   = stages.last.to_s
    benign = BENIGN_EXIT.any? { |rx, codes| last.match?(rx) && codes.include?(exit_code) }
    benign ||= stages.length > 1 && BENIGN_EXIT.any? { |rx, codes| stages.any? { |s| s.match?(rx) } && codes.include?(exit_code) && stderr.strip.empty? }
    shape = recoverable_shape(exit_code: exit_code, stderr: stderr, err: err)
  elsif !ok
    shape = recoverable_shape(exit_code: exit_code, stderr: stderr, err: err || raw[0, 200])
  end

  semantic = ok && (exit_code.nil? || exit_code.zero? || benign)
  err ||= raw[/"stderr":"([^"]{4,300})"/, 1] unless semantic
  { ok: ok, semantic_ok: semantic, exit: exit_code, err: err, benign: benign, shape: shape }
end

.sentinel ⇒ Object

Supported Method Parameters

r = PWN::AI::Agent::Reward.sentinel



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# File 'lib/pwn/ai/agent/reward.rb', line 182

public_class_method def self.sentinel
  s = normalize_sentinel(raw: load_sentinel)
  window = s[:window]
  n = window.length
  return { samples: n, status: :insufficient } if n < SENTINEL_WINDOW

  means = window_means(window: window)
  proxy = means[:proxy]
  judge = means[:judge]
  # Refuse to act on corrupt arithmetic — proxy must be a rate in [0,1].
  if proxy.nil? || proxy < 0.0 || proxy > 1.0
    return {
      samples: n,
      status: :corrupt_proxy,
      proxy: proxy,
      judge: judge&.round(3),
      reward_hacked: false,
      proxy_distrust: proxy_distrust
    }
  end

  human = 1.0 - user_correction_rate
  gap_pj = (proxy - judge).abs
  gap_ph = (proxy - human).abs
  hacked = gap_pj > SENTINEL_GAP || gap_ph > SENTINEL_GAP
  if hacked
    # 1.1 — freeze auto-Mistakes.record on tool:reward_signal after the
    # first open sig per gap-bucket. Endless ×13 fingerprints were
    # the loudest scar in every prompt and taught nothing. Open a
    # calibration path instead; park the sig as needs_code_change.
    bucket = "gap_pj=#{gap_pj.round(2)}|gap_ph=#{gap_ph.round(2)}"
    open_sig = defined?(Mistakes) ? Mistakes.for_tool(tool: 'reward_signal', unresolved_only: true) : []
    if open_sig.empty? && defined?(Mistakes)
      m = Mistakes.record(
        tool: 'reward_signal',
        error: "proxy success_rate #{proxy.round(2)} diverges from judge #{judge.round(2)} / human #{human.round(2)} by >#{SENTINEL_GAP}",
        source: :model,
        needs_code_change: true,
        meta: { bucket: bucket, proxy: proxy, judge: judge, human: human }
      )
      Mistakes.park(signature: m[:signature], reason: 'reward_signal needs calibration, not practice') if m && Mistakes.respond_to?(:park)
    end
    Curriculum.calibrate(predicted: proxy, actual: judge, engine: :reward_sentinel) if defined?(Curriculum) && Curriculum.respond_to?(:calibrate)
    # P4 — make sentinel ACTIONABLE: persist a distrust factor so
    # Metrics.to_context / Registry.rank haircut proxy success instead of
    # just opening another Mistakes row the model learns to ignore.
    set_proxy_distrust(gap: [gap_pj, gap_ph].max, proxy: proxy, judge: judge)
  else
    clear_proxy_distrust
  end
  {
    samples: n,
    proxy: proxy.round(3),
    judge: judge.round(3),
    human: human.round(3),
    gap_proxy_judge: gap_pj.round(3),
    gap_proxy_human: gap_ph.round(3),
    reward_hacked: hacked,
    proxy_distrust: proxy_distrust
  }
end

.set_proxy_distrust(opts = {}) ⇒ Object 



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# File 'lib/pwn/ai/agent/reward.rb', line 259

public_class_method def self.set_proxy_distrust(opts = {})
  s = normalize_sentinel(raw: load_sentinel)
  gap = opts[:gap].to_f
  proxy = opts[:proxy]
  # Guard: never set distrust from a nonsensical proxy (pre-ring-buffer
  # decay×to_i bug produced means ≫ 1.0 and hard-pegged distrust at 1.0).
  unless proxy.nil?
    pf = proxy.to_f
    return s[:proxy_distrust].to_f if pf < 0.0 || pf > 1.0
  end
  # map gap 0.15→0.4, 0.30→0.8, ≥0.40→1.0
  factor = ((((gap - SENTINEL_GAP) / SENTINEL_GAP) * 0.4) + 0.4).clamp(0.3, 1.0)
  s[:proxy_distrust] = factor
  s[:distrust_at] = Time.now.utc.iso8601
  s[:distrust_meta] = { proxy: opts[:proxy], judge: opts[:judge], gap: gap }
  FileUtils.mkdir_p(File.dirname(SENTINEL_FILE))
  atomic_write(path: SENTINEL_FILE, body: JSON.generate(s))
  factor
rescue StandardError
  nil
end

.verify_as_reward(opts = {}) ⇒ Object

Supported Method Parameters

g = PWN::AI::Agent::Reward.verify_as_reward(final: text)



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# File 'lib/pwn/ai/agent/reward.rb', line 373

public_class_method def self.verify_as_reward(opts = {})
  return nil unless defined?(Extrospection) && Extrospection.respond_to?(:verify)

  final = opts[:final].to_s
  claim = final[Learning::CLAIM_RX] if defined?(Learning)
  return nil if claim.to_s.empty?

  # 1.5 — sampled E3: always when flag true; never when false;
  # nil/auto → always on frontier, ~10% on local when CLAIM_RX hits.
  flag = agent_flag(key: :verify_as_reward, default: nil)
  eng  = (PWN::Env.dig(:ai, :active) if defined?(PWN::Env)).to_s.downcase
  local = eng == 'ollama'
  run = case flag
        when true then true
        when false then false
        else
          local ? (Digest::SHA256.hexdigest(claim.to_s)[0, 2].to_i(16) % 10).zero? : true
        end
  return nil unless run

  r = Extrospection.verify(claim: claim, commit: true)
  { claim: claim, verdict: r[:verdict], confidence: r[:confidence], reward: VERDICTS[r[:verdict]] || 0.5 }
rescue StandardError
  nil
end