diff --git a/common/sampling.cpp b/common/sampling.cpp
index a842d2b8..ca398400 100644
--- a/common/sampling.cpp
+++ b/common/sampling.cpp
@@ -109,8 +109,6 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co
         }
     }
 
-    result->n_rewind = -1;
-
     return result;
 }
 
@@ -147,12 +145,10 @@ void common_sampler_reset(common_sampler * ctx) {
     llama_sampler_dry_reset(ctx->smpl);
 }
 
-void common_sampler_review(common_sampler * ctx) {
-    const int32_t n_rewind = ctx->n_rewind;
-
+void common_sampler_review(common_sampler * ctx, const size_t n_unsent, const bool rewind_status) {
     // add stateful samplers here
     if (ctx->adapt_p_ctx != nullptr) {
-        llama_review_adaptive_p(ctx->adapt_p_ctx, n_rewind);
+        llama_review_adaptive_p(ctx->adapt_p_ctx, n_unsent, rewind_status);
     }
 }
 
@@ -425,13 +421,14 @@ static llama_token llama_sampling_sample_impl(
             id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu);
         } else if (adaptive_target >= 0.0f && ctx_sampling->adapt_p_ctx!=nullptr) {
             // adaptive p sampling
+            llama_prep_adaptive_p(ctx_main, &cur_p, ctx_sampling->adapt_p_ctx);
             sampler_queue(ctx_main, params, ctx_sampling, cur_p, std::max(1, params.min_keep));
             id = llama_sample_token_adaptive_p(ctx_main, &cur_p, ctx_sampling->adapt_p_ctx);
         } else {
             // temperature sampling
             size_t min_keep = std::max(1, params.min_keep);
 
-            sampler_queue(ctx_main, params,ctx_sampling, cur_p, min_keep);           
+            sampler_queue(ctx_main, params,ctx_sampling, cur_p, min_keep);
             id = llama_sample_token_with_rng(ctx_main, &cur_p, ctx_sampling->rng);
 
         }
@@ -496,11 +493,6 @@ static llama_token_data_array llama_sampling_prepare_impl(
         *original_logits = {logits, logits + n_vocab};
     }
 
-    if ((params.temp > 0) && (params.mirostat == 0) && (params.adaptive_target >= 0) && (ctx_sampling->adapt_p_ctx != nullptr)) {
-        // collect original probability before logit bias is applied
-        llama_prep_adaptive_p(ctx_main, logits, ctx_sampling->adapt_p_ctx);
-    }
-
     // apply params.logit_bias map
     for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
         logits[it->first] += it->second;
diff --git a/common/sampling.h b/common/sampling.h
index 66d9e613..7f6a3df7 100644
--- a/common/sampling.h
+++ b/common/sampling.h
@@ -134,8 +134,6 @@ struct common_sampler {
     llama_token_data_array cur_p; // current candidates
 
     std::mt19937 rng;
-
-    int32_t n_rewind;   // number of tokens to rewind
 };
 
 
@@ -152,7 +150,7 @@ void common_sampler_reset(common_sampler * ctx);
 
 // Review stateful samplers
 // - rewind internal states (maybe)
-void common_sampler_review(common_sampler * ctx);
+void common_sampler_review(common_sampler * ctx, const size_t n_unsent, const bool rewind_status);
 
 // Set the sampler seed
 void llama_sampling_set_rng_seed(struct common_sampler * ctx, uint32_t seed);
diff --git a/examples/server/server-context.cpp b/examples/server/server-context.cpp
index 28c0cbc3..627a3f99 100644
--- a/examples/server/server-context.cpp
+++ b/examples/server/server-context.cpp
@@ -882,6 +882,7 @@ bool server_context::launch_slot_with_task(server_slot& slot, server_task& task)
     slot.sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta);
     slot.sparams.adaptive_target = json_value(data, "adaptive_target", default_sparams.adaptive_target);
     slot.sparams.adaptive_decay = json_value(data, "adaptive_decay", default_sparams.adaptive_decay);
+    slot.sparams.adaptive_updt_w_cur = json_value(data, "adaptive_updt_w_cur", default_sparams.adaptive_updt_w_cur);
     slot.sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl);
     slot.params.n_keep = json_value(data, "n_keep", slot.params.n_keep);
     slot.params.n_discard = json_value(data, "n_discard", defaults.n_discard);
@@ -1667,6 +1668,7 @@ json server_context::get_formated_generation(const server_slot& slot) const {
         {"mirostat_eta",              slot.sparams.mirostat_eta},
         {"adaptive_target",           slot.sparams.adaptive_target},
         {"adaptive_decay",            slot.sparams.adaptive_decay},
+        {"adaptive_updt_w_cur",       slot.sparams.adaptive_updt_w_cur},
         {"penalize_nl",               slot.sparams.penalize_nl},
         {"stop",                      slot.params.antiprompt},
         {"max_tokens",                slot.params.n_predict}, // User configured n_predict
@@ -3332,7 +3334,7 @@ void server_context::speculative_decoding_accept() {
                 buffer_and_check_string_ban(slot, result);
             }
 
-            common_sampler_review(slot.ctx_sampling);
+            common_sampler_review(slot.ctx_sampling, slot.token_buffer.size(), slot.rewind_status);
         }
         SLT_DBG(slot, "accepted %d/%d draft tokens, new n_tokens = %d\n", (int)ids.size() - 1, (int)slot.drafted.size(), slot.n_past);
         LOG_VERBOSE("speculative decoding result", {
@@ -3545,7 +3547,6 @@ void server_context::buffer_and_check_string_ban(server_slot & slot, completion_
     bool buffer_full = slot.token_buffer.size() >= slot.n_buffer;
 
     int32_t ban_pos = -1;
-    int32_t n_rewind = 0;
     bool sent_results = false;
 
     // Always reset logit bias to base before checking bans
@@ -3553,12 +3554,6 @@ void server_context::buffer_and_check_string_ban(server_slot & slot, completion_
 
     if (slot.ban_phrases.size() > 0 || slot.ban_regex.size() > 0 || slot.ban_regex_ci.size() > 0) {
         ban_pos = check_ban_phrase(slot);
-        if (ban_pos >= 0 && slot.sparams.adaptive_target >= 0.0f) {
-            int32_t buffer_start_pos = slot.n_past - (int32_t)slot.token_buffer.size() + 1;
-            int32_t n_keep_buffer = ban_pos - buffer_start_pos;
-            if (n_keep_buffer < 0) n_keep_buffer = 0;
-            n_rewind = (int32_t)slot.token_buffer.size() - n_keep_buffer;
-    }
     }
 
     bool allow_rewind = true;
@@ -3600,17 +3595,11 @@ void server_context::buffer_and_check_string_ban(server_slot & slot, completion_
             // send 1 token from the front (FIFO)
             send_token_results(slot.token_buffer, slot, 1);
         }
-        if (slot.sparams.adaptive_target >= 0.0f) {
-        sent_results = true;
-    }
     }
     else {
         // buffer the result, wait for more tokens to validate string
         slot.sampled = result.tok; 
     }
-    if (slot.sparams.adaptive_target >= 0.0f) {
-    slot.ctx_sampling->n_rewind = sent_results ? -1 : n_rewind;
-}
 }
 
 void server_context::process_batch_tokens(int32_t & n_batch) {
@@ -3761,7 +3750,7 @@ void server_context::process_batch_tokens(int32_t & n_batch) {
                 buffer_and_check_string_ban(slot, result);
             }
 
-            common_sampler_review(slot.ctx_sampling);
+            common_sampler_review(slot.ctx_sampling, slot.token_buffer.size(), slot.rewind_status);
 
             slot.i_batch = -1;
         }
diff --git a/include/llama.h b/include/llama.h
index 34e25402..f1729662 100644
--- a/include/llama.h
+++ b/include/llama.h
@@ -1406,7 +1406,7 @@ LLAMA_API struct llama_grammar* llama_sampler_init_grammar_lazy_patterns(
         const uint32_t seed);
 
     void llama_prep_adaptive_p(struct llama_context * ctx,
-                                  float * logits,
+                 llama_token_data_array * candidates,
         struct llama_sampler_adaptive_p * adapt_p_ctx);
 
     /// @details Adaptive p sampler described in https://github.com/MrJackSpade/adaptive-p-docs/blob/main/README.md
@@ -1414,7 +1414,7 @@ LLAMA_API struct llama_grammar* llama_sampler_init_grammar_lazy_patterns(
                                llama_token_data_array * candidates,
                       struct llama_sampler_adaptive_p * adapt_p_ctx);
 
-    void llama_review_adaptive_p(struct llama_sampler_adaptive_p * adapt_p_ctx, const int32_t n_rewind);
+    void llama_review_adaptive_p(struct llama_sampler_adaptive_p * adapt_p_ctx, const size_t n_unsent, const bool rewind_status);
 
 
     /// @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
diff --git a/src/llama-sampling.cpp b/src/llama-sampling.cpp
index 1663312d..a503fcc6 100644
--- a/src/llama-sampling.cpp
+++ b/src/llama-sampling.cpp
@@ -1046,49 +1046,30 @@ struct llama_sampler_dry* llama_sampler_init_dry_impl(const struct llama_vocab&
 
 // adaptive p
 
-void llama_review_adaptive_p_impl(llama_sampler_adaptive_p * adapt_p_ctx, const int32_t n_rewind) {
-    if ((n_rewind == 0) || (adapt_p_ctx->target < 0.0f)) {
-        return;
-    }
-    // auto & weighted_sum = adapt_p_ctx->weighted_sum;
-    // auto & total_weight = adapt_p_ctx->total_weight;
-
-    const int32_t sz = adapt_p_ctx->history.size();
-    if ((sz <= 0) || (sz <= n_rewind)) {
-        // critically short history. reset to initial state
-        LLAMA_LOG_WARN("%s: sz=%d, n_rewind=%d should not be possible\n", __func__, sz, n_rewind);
-        adapt_p_ctx->history.clear();
-        adapt_p_ctx->history.push_back({
-            adapt_p_ctx->target / adapt_p_ctx->decay,   // weighted_sum
-            1.0f / adapt_p_ctx->decay });               // total_weight
+void llama_review_adaptive_p_impl(llama_sampler_adaptive_p * adapt_p_ctx, const size_t n_unsent, const bool rewind_status) {
+    // LLAMA_LOG_DEBUG("%s: n_unsent = %zu, rewind_status = %s\n", __func__, n_unsent, rewind_status ? "true" : "false");
+    if (adapt_p_ctx->target < 0.0f) {
+        // LLAMA_LOG_DEBUG("%s: sampler disabled, target = %f\n", __func__, adapt_p_ctx->target);
         return;
     }
 
-    if (n_rewind < 0) {
-        // clear history except most recent
-        adapt_p_ctx->history.front() = adapt_p_ctx->history.back();
-        adapt_p_ctx->history.resize(1);
-    } else {
-        // rewind
-        adapt_p_ctx->history.resize(sz - n_rewind);
+    auto & history = adapt_p_ctx->history;
+    const size_t hsz = history.size();
+    const size_t hsz_next = 1 + n_unsent;
+    // LLAMA_LOG_DEBUG("%s: hsz = %zu, hsz_next = %zu\n", __func__, hsz, hsz_next);
+    if (hsz_next >= hsz >> 1) { return; }   // skip small update
 
-        // int32_t sz = weighted_sum.size() - n_rewind;
-        // if (sz > 0) {
-        //     weighted_sum.resize(sz);
-        // } else {
-        //     LLAMA_LOG_WARN("%s: n_rewind=%d, sz=%d should not be possible\n", __func__, n_rewind, sz);
-        //     weighted_sum.clear();
-        //     weighted_sum.push_back(adapt_p_ctx->target / adapt_p_ctx->decay);   // set to default value
-        // }
-        // sz = total_weight.size() - n_rewind;
-        // if (sz > 0) {
-        //     total_weight.resize(sz);
-        // } else {
-        //     LLAMA_LOG_WARN("%s: n_rewind=%d, sz=%d should not be possible\n", __func__, n_rewind, sz);
-        //     total_weight.clear();
-        //     total_weight.push_back(1.0f / adapt_p_ctx->decay);  // set to default value
-        // }
+    if (!rewind_status) {
+        // sent results, overwrite old history
+        // LLAMA_LOG_DEBUG("%s: hsz = %zu, hsz_next = %zu\n", __func__, hsz, hsz_next);
+        // LLAMA_LOG_DEBUG("%s: history[hsz-1].first = %f\n", __func__, history[hsz-1].first);
+        const size_t hsz_diff = hsz - hsz_next;
+        for (int j = 0; j < hsz_next; ++j) {
+            history[j] = history[j + hsz_diff];
+        }
     }
+    history.resize(hsz_next);
+    // LLAMA_LOG_DEBUG("%s: history[hsz_next-1].first = %f\n", __func__, history[hsz_next-1].first);
 }
 
 llama_token llama_sample_token_adaptive_p_impl(
@@ -1126,8 +1107,6 @@ llama_token llama_sample_token_adaptive_p_impl(
         ctx->history.push_back({
             ctx->decay * ctx->history.back().first + update_prob,   // weighted_sum
             ctx->decay * ctx->history.back().second + 1.0f });      // total_weight
-        // ctx->weighted_sum.push_back(ctx->decay * ctx->weighted_sum.back() + update_prob);
-        // ctx->total_weight.push_back(ctx->decay * ctx->total_weight.back() + 1.0f);
     }
 
     smpl->t_sample_us += ggml_time_us() - t_start_sample_us;
@@ -1139,7 +1118,7 @@ llama_token llama_sample_token_adaptive_p_impl(
 void llama_sample_adaptive_p_impl(struct llama_sampling * ctx, llama_token_data_array * candidates,
         struct llama_sampler_adaptive_p * adapt_p_ctx) {
     if (adapt_p_ctx->target < 0.0f) {
-        // sampler is disabled
+        // LLAMA_LOG_DEBUG("%s: sampler disabled, target = %f\n", __func__, adapt_p_ctx->target);
         llama_sample_softmax_impl(nullptr, candidates);
         return;
     }
@@ -1195,20 +1174,25 @@ void llama_sample_adaptive_p_impl(struct llama_sampling * ctx, llama_token_data_
 
 void llama_prep_adaptive_p_impl(
               struct llama_sampling * smpl,
-                              float * logits,
+             llama_token_data_array * candidates,
     struct llama_sampler_adaptive_p * adapt_p_ctx) {
-    if (adapt_p_ctx->updt_w_cur) {
-        // update with current probability, original not needed
+    if (adapt_p_ctx->updt_w_cur     // update with current probability, original not needed
+        || (adapt_p_ctx->target < 0.0f)) {  // or disabled
         return;
     }
     constexpr float kDelta = 30.0f; //16.6f;
     auto t_start = ggml_time_us();
     auto & orig_prob = adapt_p_ctx->orig_prob;
-
-    std::copy(logits, logits + orig_prob.size(), orig_prob.begin());
+    if (candidates->size != orig_prob.size() || candidates->sorted) {
+        LLAMA_LOG_ERROR("%s: this function must be called before any other sampler has been applied\n", __func__);
+        LLAMA_LOG_ERROR("%s: the sampler has been initialized with a vocabulary of %zu, but is being called with %zu candidates\n",
+                __func__, orig_prob.size(), candidates->size);
+        GGML_ABORT("Bad candidates in adaptive_p sampler");
+    }
 
     float max_logit = -INFINITY;
-    for (int j = 0; j < int(orig_prob.size()); ++j) {
+    for (int j = 0; j < int(candidates->size); ++j) {
+        orig_prob[j] = candidates->data[j].logit;
         max_logit = std::max(max_logit, orig_prob[j]);
     }
     adapt_p_ctx->cum_orig_prob = iqk_exp_with_thresh(orig_prob.size(), orig_prob.data(), max_logit, max_logit - kDelta);
@@ -1228,8 +1212,6 @@ struct llama_sampler_adaptive_p * llama_init_adaptive_p_impl(int n_vocab,
         /* .decay             = */ clamped_decay,
         /* .updt_w_cur        = */ updt_w_cur,
         /* .rng               = */ std::mt19937(seed),
-        // /* .weighted_sum      = */ {},
-        // /* .total_weight      = */ {},
         /* .history           = */ {},
         /* .orig_prob         = */ {},
         /* .cum_orig_prob     = */ 1.0f,
@@ -1237,8 +1219,6 @@ struct llama_sampler_adaptive_p * llama_init_adaptive_p_impl(int n_vocab,
         /* .max_xform_logit   = */ -INFINITY,
         /* .cum_probs         = */ {},
     };
-    // result->weighted_sum.push_back(target / (1.0f - clamped_decay));
-    // result->total_weight.push_back(1.0f / (1.0f - clamped_decay));
     result->history.push_back({
         target / (1.0f - clamped_decay),    // weighted_sum
         1.0f / (1.0f - clamped_decay) });   // total_weight
diff --git a/src/llama-sampling.h b/src/llama-sampling.h
index 6127a50f..80d5a4f2 100644
--- a/src/llama-sampling.h
+++ b/src/llama-sampling.h
@@ -70,8 +70,6 @@ struct llama_sampler_adaptive_p {
     const float decay;      // EMA decay; history ≈ 1/(1-decay) tokens (0.0 - 0.99)
     const bool updt_w_cur;  // false=original, true=current
     std::mt19937 rng;       // RNG
-    // std::vector<float> weighted_sum;    // [0] = sum(p_n * decay^N)
-    // std::vector<float> total_weight;    // [0] = sum(decay^i), converges to 1/(1-decay)
     std::vector<std::pair<float, float>> history;   // <weighted_sum, total_weight>
 
     // first referenced in prep
@@ -94,7 +92,7 @@ struct llama_sampler_adaptive_p * llama_init_adaptive_p_impl(int n_vocab,
 
 void llama_prep_adaptive_p_impl(
               struct llama_sampling * smpl,
-                              float * logits,
+             llama_token_data_array * candidates,
     struct llama_sampler_adaptive_p * adapt_p_ctx);
 
 void llama_sample_adaptive_p_impl(
@@ -102,7 +100,7 @@ void llama_sample_adaptive_p_impl(
              llama_token_data_array * candidates,
     struct llama_sampler_adaptive_p * adapt_p_ctx);
 
-void llama_review_adaptive_p_impl(llama_sampler_adaptive_p * adapt_p_ctx, const int32_t n_rewind);
+void llama_review_adaptive_p_impl(llama_sampler_adaptive_p * adapt_p_ctx, const size_t n_unsent, const bool rewind_status);
 
 
 void llama_sample_repetition_penalties_impl(
diff --git a/src/llama.cpp b/src/llama.cpp
index 6792145f..1090bee0 100644
--- a/src/llama.cpp
+++ b/src/llama.cpp
@@ -8281,8 +8281,8 @@ void llama_sample_adaptive_p(llama_context * ctx,
     llama_sample_adaptive_p_impl(&ctx->sampling, candidates, adapt_p_ctx);
 }
 
-void llama_prep_adaptive_p(struct llama_context * ctx, float * logits, struct llama_sampler_adaptive_p * adapt_p_ctx) {
-    llama_prep_adaptive_p_impl(&ctx->sampling, logits, adapt_p_ctx);
+void llama_prep_adaptive_p(struct llama_context * ctx, llama_token_data_array * candidates, struct llama_sampler_adaptive_p * adapt_p_ctx) {
+    llama_prep_adaptive_p_impl(&ctx->sampling, candidates, adapt_p_ctx);
 }
 
 
@@ -8388,8 +8388,8 @@ struct llama_sampler_adaptive_p * llama_init_adaptive_p(int n_vocab, const float
     return llama_init_adaptive_p_impl(n_vocab, target, decay, updt_w_cur, seed);
 }
 
-void llama_review_adaptive_p(struct llama_sampler_adaptive_p * adapt_p_ctx, const int32_t n_rewind) {
-    llama_review_adaptive_p_impl(adapt_p_ctx, n_rewind);
+void llama_review_adaptive_p(struct llama_sampler_adaptive_p * adapt_p_ctx, const size_t n_unsent, const bool rewind_status) {
+    llama_review_adaptive_p_impl(adapt_p_ctx, n_unsent, rewind_status);
 }